analyze_simulations_downsampled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af4f886c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/p7/5y4rzfc927v9625g5xrx460crwqrl3/T/ipykernel_48204/1872962086.py:7: DeprecationWarning: \n",
      "The `fooof` package is being deprecated and replaced by the `specparam` (spectral parameterization) package.\n",
      "This version of `fooof` (1.1) is fully functional, but will not be further updated.\n",
      "New projects are recommended to update to using `specparam` (see Changelog for details).\n",
      "  from fooof import FOOOFGroup\n",
      "Warning: no DISPLAY environment variable.\n",
      "--No graphics will be displayed.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import scipy\n",
    "\n",
    "# how to change this from fooof (1.x.x) to specparam (2.0.0)\n",
    "from fooof import FOOOFGroup\n",
    "from fooof.sim.gen import gen_aperiodic\n",
    "\n",
    "from model import create_model\n",
    "from scipy.stats import linregress\n",
    "from scipy.signal import decimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d6a67d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_trials = 100\n",
    "n_electrodes = 25\n",
    "\n",
    "# @Carolina, how to change the colors and shapes of the neurons and electrodes?\n",
    "net_ds = create_model()\n",
    "_ = net_ds.plot_cells()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3cca474a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and downsample model LFPs\n",
    "srate_orig = 40000\n",
    "srate = 2000\n",
    "dwnsmpl_fctr = int(srate_orig / srate)\n",
    "ds_trial_signals = np.load('shank_ds.npy')\n",
    "ds_trial_signals = decimate(ds_trial_signals, dwnsmpl_fctr, n=None, ftype='fir', axis=-1)\n",
    "us_trial_signals = np.load('shank_us.npy')\n",
    "us_trial_signals = decimate(us_trial_signals, dwnsmpl_fctr, n=None, ftype='fir', axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5a86154",
   "metadata": {},
   "outputs": [],
   "source": [
    "winsize = 128\n",
    "overlap = int(winsize*7/8)\n",
    "fft_length = int(winsize*16)\n",
    "freq_lims = [0.5, 250]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10e551b",
   "metadata": {},
   "source": [
    "**Compute power spectra over whole trial**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a4ac7a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.9765625   1.953125    2.9296875   3.90625     4.8828125   5.859375\n",
      "   6.8359375   7.8125      8.7890625   9.765625   10.7421875  11.71875\n",
      "  12.6953125  13.671875   14.6484375  15.625      16.6015625  17.578125\n",
      "  18.5546875  19.53125    20.5078125  21.484375   22.4609375  23.4375\n",
      "  24.4140625  25.390625   26.3671875  27.34375    28.3203125  29.296875\n",
      "  30.2734375  31.25       32.2265625  33.203125   34.1796875  35.15625\n",
      "  36.1328125  37.109375   38.0859375  39.0625     40.0390625  41.015625\n",
      "  41.9921875  42.96875    43.9453125  44.921875   45.8984375  46.875\n",
      "  47.8515625  48.828125   49.8046875  50.78125    51.7578125  52.734375\n",
      "  53.7109375  54.6875     55.6640625  56.640625   57.6171875  58.59375\n",
      "  59.5703125  60.546875   61.5234375  62.5        63.4765625  64.453125\n",
      "  65.4296875  66.40625    67.3828125  68.359375   69.3359375  70.3125\n",
      "  71.2890625  72.265625   73.2421875  74.21875    75.1953125  76.171875\n",
      "  77.1484375  78.125      79.1015625  80.078125   81.0546875  82.03125\n",
      "  83.0078125  83.984375   84.9609375  85.9375     86.9140625  87.890625\n",
      "  88.8671875  89.84375    90.8203125  91.796875   92.7734375  93.75\n",
      "  94.7265625  95.703125   96.6796875  97.65625    98.6328125  99.609375\n",
      " 100.5859375 101.5625    102.5390625 103.515625  104.4921875 105.46875\n",
      " 106.4453125 107.421875  108.3984375 109.375     110.3515625 111.328125\n",
      " 112.3046875 113.28125   114.2578125 115.234375  116.2109375 117.1875\n",
      " 118.1640625 119.140625  120.1171875 121.09375   122.0703125 123.046875\n",
      " 124.0234375 125.        125.9765625 126.953125  127.9296875 128.90625\n",
      " 129.8828125 130.859375  131.8359375 132.8125    133.7890625 134.765625\n",
      " 135.7421875 136.71875   137.6953125 138.671875  139.6484375 140.625\n",
      " 141.6015625 142.578125  143.5546875 144.53125   145.5078125 146.484375\n",
      " 147.4609375 148.4375    149.4140625 150.390625  151.3671875 152.34375\n",
      " 153.3203125 154.296875  155.2734375 156.25      157.2265625 158.203125\n",
      " 159.1796875 160.15625   161.1328125 162.109375  163.0859375 164.0625\n",
      " 165.0390625 166.015625  166.9921875 167.96875   168.9453125 169.921875\n",
      " 170.8984375 171.875     172.8515625 173.828125  174.8046875 175.78125\n",
      " 176.7578125 177.734375  178.7109375 179.6875    180.6640625 181.640625\n",
      " 182.6171875 183.59375   184.5703125 185.546875  186.5234375 187.5\n",
      " 188.4765625 189.453125  190.4296875 191.40625   192.3828125 193.359375\n",
      " 194.3359375 195.3125    196.2890625 197.265625  198.2421875 199.21875\n",
      " 200.1953125 201.171875  202.1484375 203.125     204.1015625 205.078125\n",
      " 206.0546875 207.03125   208.0078125 208.984375  209.9609375 210.9375\n",
      " 211.9140625 212.890625  213.8671875 214.84375   215.8203125 216.796875\n",
      " 217.7734375 218.75      219.7265625 220.703125  221.6796875 222.65625\n",
      " 223.6328125 224.609375  225.5859375 226.5625    227.5390625 228.515625\n",
      " 229.4921875 230.46875   231.4453125 232.421875  233.3984375 234.375\n",
      " 235.3515625 236.328125  237.3046875 238.28125   239.2578125 240.234375\n",
      " 241.2109375 242.1875    243.1640625 244.140625  245.1171875 246.09375\n",
      " 247.0703125 248.046875  249.0234375]\n",
      "[0.    0.008 0.016 0.024 0.032 0.04  0.048 0.056 0.064 0.072 0.08  0.088\n",
      " 0.096 0.104 0.112 0.12  0.128 0.136 0.144 0.152 0.16  0.168 0.176 0.184\n",
      " 0.192 0.2   0.208 0.216 0.224 0.232 0.24  0.248 0.256 0.264 0.272 0.28\n",
      " 0.288 0.296 0.304 0.312 0.32  0.328 0.336 0.344 0.352 0.36  0.368 0.376\n",
      " 0.384 0.392 0.4   0.408 0.416 0.424 0.432 0.44  0.448 0.456]\n",
      "(100, 25, 1025, 58)\n"
     ]
    }
   ],
   "source": [
    "# Compute power spectral density from STFT - detected stimuli\n",
    "freqs, times, Zxx = scipy.signal.stft(ds_trial_signals, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=fft_length, detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "ds_layer_psd = np.abs(Zxx) ** 2\n",
    "f_idx = (freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs = freqs[f_idx]\n",
    "ds_layer_psd = ds_layer_psd[:, :, f_idx, :]\n",
    "mds_layer_psd = np.mean(ds_layer_psd, axis=-1)\n",
    "\n",
    "# Compute power spectral density from STFT - undetected stimuli\n",
    "freqs, times, Zxx = scipy.signal.stft(us_trial_signals, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=fft_length, detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "us_layer_psd = np.abs(Zxx) ** 2\n",
    "f_idx = (freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs = freqs[f_idx]\n",
    "us_layer_psd = us_layer_psd[:, :, f_idx, :]\n",
    "mus_layer_psd = np.mean(us_layer_psd, axis=-1)\n",
    "\n",
    "print(freqs)\n",
    "print(times)\n",
    "print(np.shape(Zxx))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0faae39",
   "metadata": {},
   "source": [
    "**Parameterize power spectra**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f6b0ec7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.98\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Parameterize detected spectra\n",
    "ds_layer_offsets = np.zeros((n_trials, n_electrodes))\n",
    "ds_layer_slopes = np.zeros((n_trials, n_electrodes))\n",
    "ds_layer_aperiodic = np.zeros((n_trials, n_electrodes, len(freqs)))\n",
    "for t in range(mds_layer_psd.shape[0]):\n",
    "    trial_psd = mds_layer_psd[t, :, :]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs,trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        ds_layer_offsets[t, i] = fg[i].aperiodic_params[0]\n",
    "        ds_layer_slopes[t, i] = fg[i].aperiodic_params[1]\n",
    "        ds_layer_aperiodic[t, i, :] = gen_aperiodic(freqs, fg[i].aperiodic_params)\n",
    "\n",
    "# Parameterize undetected spectra\n",
    "us_layer_offsets = np.zeros((n_trials, n_electrodes))\n",
    "us_layer_slopes = np.zeros((n_trials, n_electrodes))\n",
    "us_layer_aperiodic = np.zeros((n_trials, n_electrodes,len(freqs)))\n",
    "for t in range(mus_layer_psd.shape[0]):\n",
    "    trial_psd = mus_layer_psd[t, :, :]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs, trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        us_layer_offsets[t, i] = fg[i].aperiodic_params[0]\n",
    "        us_layer_slopes[t, i] = fg[i].aperiodic_params[1]\n",
    "        us_layer_aperiodic[t, i, :] = gen_aperiodic(freqs, fg[i].aperiodic_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4b6dde",
   "metadata": {},
   "source": [
    "**Plot spectra and parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1b70e2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.size': 14,\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.top': False,\n",
    "    'xtick.major.size': 6,\n",
    "    'xtick.major.width': 1.2,\n",
    "    'ytick.major.size': 6,\n",
    "    'ytick.major.width': 1.2,\n",
    "    'legend.frameon': False,\n",
    "    'legend.handletextpad': 0.1,\n",
    "    'svg.fonttype': 'none',\n",
    "    'text.usetex': False\n",
    "})\n",
    "\n",
    "fig=plt.figure(figsize=(12,4))\n",
    "undetected_color=np.array([0,76.5,99.2])/100\n",
    "detected_color=np.array([98.4,54.1,1.2])/100\n",
    "\n",
    "gs = GridSpec(1, 4, width_ratios=[.275, .275, .225, .225])\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "#m_detected=np.mean(ds_layer_aperiodic,axis=0)\n",
    "m_detected=np.log10(np.mean(mds_layer_psd,axis=0))\n",
    "ax1.set_xscale('log')\n",
    "#ax1.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax1.plot(freqs,m_detected[i,:],color=detected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "# ax1.set_xlim([.1,125])\n",
    "ax1.set_xlabel('Frequency (Hz)')\n",
    "ax1.set_ylabel('Power')\n",
    "    \n",
    "ax2=fig.add_subplot(gs[1])\n",
    "# m_undetected=np.mean(us_layer_aperiodic,axis=0)\n",
    "m_undetected=np.log10(np.mean(mus_layer_psd,axis=0))\n",
    "ax2.set_xscale('log')\n",
    "# ax2.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax2.plot(freqs,m_undetected[i,:],color=undetected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "#ax2.set_xlim([.1,125])\n",
    "ax2.set_xlabel('Frequency (Hz)')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "m_detected=np.mean(ds_layer_offsets,axis=0)\n",
    "se_detected=np.std(ds_layer_offsets,axis=0)\n",
    "ax3.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_offsets,axis=0)\n",
    "se_undetected=np.std(us_layer_offsets,axis=0)\n",
    "ax3.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax3.set_ylim([n_electrodes-1,0])\n",
    "ax3.set_ylabel('Virtual contact')\n",
    "ax3.set_xlabel('Offset')\n",
    "ax3.legend(loc='lower right')\n",
    "\n",
    "ax4=fig.add_subplot(gs[3])\n",
    "m_detected=np.mean(ds_layer_slopes,axis=0)\n",
    "se_detected=np.std(ds_layer_slopes,axis=0)\n",
    "ax4.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_slopes,axis=0)\n",
    "se_undetected=np.std(us_layer_slopes,axis=0)\n",
    "ax4.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax4.set_ylim([n_electrodes-1,0])\n",
    "ax4.set_xlabel('Slope')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('aan-e_i.svg')\n",
    "# plt.savefig('aan-e_i.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e1d4543",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "m_detected=np.log10(np.mean(mds_layer_psd,axis=0))\n",
    "m_undetected=np.log10(np.mean(mus_layer_psd,axis=0))\n",
    "im=plt.imshow(m_detected-m_undetected,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Log Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d46c89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    ds_rel_power[:,i]=m_detected[:,i]/np.max(m_detected[:,i])\n",
    "im=plt.imshow(ds_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Detected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3716a116",
   "metadata": {},
   "outputs": [],
   "source": [
    "us_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    us_rel_power[:,i]=m_undetected[:,i]/np.max(m_undetected[:,i])\n",
    "im=plt.imshow(us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b3dbc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "im=plt.imshow(ds_rel_power-us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8bd35d",
   "metadata": {},
   "source": [
    "**Compute power spectra over whole trial after subtracting out mean response**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e54e193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute power spectral density from STFT after subtracting mean - detected stimuli\n",
    "ds_mean=np.mean(ds_trial_signals,axis=0)\n",
    "freqs, times, Zxx = scipy.signal.stft(ds_trial_signals-ds_mean, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=int(winsize * 20), detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "ds_layer_resid_psd = np.abs(Zxx) ** 2\n",
    "f_idx=(freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs=freqs[f_idx]\n",
    "ds_layer_resid_psd=ds_layer_resid_psd[:,:,f_idx,:]\n",
    "mds_layer_resid_psd=np.mean(ds_layer_resid_psd,axis=-1)\n",
    "\n",
    "\n",
    "# Compute power spectral density from STFT after subtracting mean - undetected stimuli\n",
    "us_mean=np.mean(us_trial_signals,axis=0)\n",
    "freqs, times, Zxx = scipy.signal.stft(us_trial_signals-us_mean, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=int(winsize * 20), detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "us_layer_resid_psd = np.abs(Zxx) ** 2\n",
    "f_idx=(freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs=freqs[f_idx]\n",
    "us_layer_resid_psd=us_layer_resid_psd[:,:,f_idx,:]\n",
    "mus_layer_resid_psd=np.mean(us_layer_resid_psd,axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b234e0",
   "metadata": {},
   "source": [
    "**Parameterize power spectra**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2bc0df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ds_layer_resid_offsets=np.zeros((n_trials,n_electrodes))\n",
    "ds_layer_resid_slopes=np.zeros((n_trials,n_electrodes))\n",
    "ds_layer_resid_aperiodic=np.zeros((n_trials,n_electrodes,len(freqs)))\n",
    "for t in range(mds_layer_resid_psd.shape[0]):\n",
    "    trial_psd=mds_layer_resid_psd[t,:,:]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs,trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        ds_layer_resid_offsets[t,i]=fg[i].aperiodic_params[0]\n",
    "        ds_layer_resid_slopes[t,i]=fg[i].aperiodic_params[1]\n",
    "        ds_layer_resid_aperiodic[t,i,:]=gen_aperiodic(freqs,fg[i].aperiodic_params)\n",
    "\n",
    "us_layer_resid_offsets=np.zeros((n_trials,n_electrodes))\n",
    "us_layer_resid_slopes=np.zeros((n_trials,n_electrodes))\n",
    "us_layer_resid_aperiodic=np.zeros((n_trials,n_electrodes,len(freqs)))\n",
    "for t in range(mus_layer_resid_psd.shape[0]):\n",
    "    trial_psd=mus_layer_resid_psd[t,:,:]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs,trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        us_layer_resid_offsets[t,i]=fg[i].aperiodic_params[0]\n",
    "        us_layer_resid_slopes[t,i]=fg[i].aperiodic_params[1]\n",
    "        us_layer_resid_aperiodic[t,i,:]=gen_aperiodic(freqs,fg[i].aperiodic_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c23ac1",
   "metadata": {},
   "source": [
    "**Plot spectra and parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29af8e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.size': 14,\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.top': False,\n",
    "    'xtick.major.size': 6,\n",
    "    'xtick.major.width': 1.2,\n",
    "    'ytick.major.size': 6,\n",
    "    'ytick.major.width': 1.2,\n",
    "    'legend.frameon': False,\n",
    "    'legend.handletextpad': 0.1,\n",
    "    'svg.fonttype': 'none',\n",
    "    'text.usetex': False\n",
    "})\n",
    "\n",
    "fig=plt.figure(figsize=(12,4))\n",
    "undetected_color=np.array([0,76.5,99.2])/100\n",
    "detected_color=np.array([98.4,54.1,1.2])/100\n",
    "\n",
    "gs = GridSpec(1, 4, width_ratios=[.275, .275, .225, .225])\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "#m_detected=np.mean(ds_layer_aperiodic,axis=0)\n",
    "m_detected=np.log10(np.mean(mds_layer_resid_psd,axis=0))\n",
    "ax1.set_xscale('log')\n",
    "# ax1.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax1.plot(freqs,m_detected[i,:],color=detected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "# ax1.set_xlim([.1,125])\n",
    "ax1.set_xlabel('Frequency (Hz)')\n",
    "ax1.set_ylabel('Power')\n",
    "    \n",
    "ax2=fig.add_subplot(gs[1])\n",
    "# m_undetected=np.mean(us_layer_aperiodic,axis=0)\n",
    "m_undetected=np.log10(np.mean(mus_layer_resid_psd,axis=0))\n",
    "ax2.set_xscale('log')\n",
    "# ax2.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax2.plot(freqs,m_undetected[i,:],color=undetected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "#ax2.set_xlim([.1,125])\n",
    "ax2.set_xlabel('Frequency (Hz)')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "m_detected=np.mean(ds_layer_resid_offsets,axis=0)\n",
    "se_detected=np.std(ds_layer_resid_offsets,axis=0)\n",
    "ax3.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_resid_offsets,axis=0)\n",
    "se_undetected=np.std(us_layer_resid_offsets,axis=0)\n",
    "ax3.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax3.set_ylim([n_electrodes-1,0])\n",
    "ax3.set_ylabel('Virtual contact')\n",
    "ax3.set_xlabel('Offset')\n",
    "ax3.legend(loc='lower right')\n",
    "\n",
    "ax4=fig.add_subplot(gs[3])\n",
    "m_detected=np.mean(ds_layer_resid_slopes,axis=0)\n",
    "se_detected=np.std(ds_layer_resid_slopes,axis=0)\n",
    "ax4.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_resid_slopes,axis=0)\n",
    "se_undetected=np.std(us_layer_resid_slopes,axis=0)\n",
    "ax4.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax4.set_ylim([n_electrodes-1,0])\n",
    "ax4.set_xlabel('Slope')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('aan-e_i.svg')\n",
    "# plt.savefig('aan-e_i.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8698eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "m_detected=np.log10(np.mean(mds_layer_resid_psd,axis=0))\n",
    "m_undetected=np.log10(np.mean(mus_layer_resid_psd,axis=0))\n",
    "im=plt.imshow(m_detected-m_undetected,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Log Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c6dad7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    ds_rel_power[:,i]=m_detected[:,i]/np.max(m_detected[:,i])\n",
    "im=plt.imshow(ds_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Detected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3ede10",
   "metadata": {},
   "outputs": [],
   "source": [
    "us_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    us_rel_power[:,i]=m_undetected[:,i]/np.max(m_undetected[:,i])\n",
    "im=plt.imshow(us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f28237a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "im=plt.imshow(ds_rel_power-us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9b9323",
   "metadata": {},
   "source": [
    "**Compute power spectra over whole trial after regressing out mean response**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2c970e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute power spectral density from STFT after regressing out mean - detected stimuli\n",
    "ds_mean=np.mean(ds_trial_signals,axis=0)\n",
    "ds_trial_resids=np.zeros(ds_trial_signals.shape)\n",
    "for t_idx in range(n_trials):\n",
    "    for e_idx in range(n_electrodes):\n",
    "        # Regress out ERF\n",
    "        slope, intercept, r, p, se = linregress(ds_mean[e_idx,:], ds_trial_signals[t_idx, e_idx, :])\n",
    "        ds_trial_resids[t_idx,e_idx,:] = ds_trial_signals[t_idx, e_idx, :] - (intercept + slope * ds_mean[e_idx,:])\n",
    "freqs, times, Zxx = scipy.signal.stft(ds_trial_resids, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=int(winsize * 20), detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "ds_layer_resid_psd = np.abs(Zxx) ** 2\n",
    "f_idx=(freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs=freqs[f_idx]\n",
    "ds_layer_resid_psd=ds_layer_resid_psd[:,:,f_idx,:]\n",
    "mds_layer_resid_psd=np.mean(ds_layer_resid_psd,axis=-1)\n",
    "\n",
    "# Compute power spectral density from STFT after regressing out mean - undetected stimuli\n",
    "us_mean=np.mean(us_trial_signals,axis=0)\n",
    "us_trial_resids=np.zeros(us_trial_signals.shape)\n",
    "for t_idx in range(n_trials):\n",
    "    for e_idx in range(n_electrodes):\n",
    "        # Regress out ERF\n",
    "        slope, intercept, r, p, se = linregress(us_mean[e_idx,:], us_trial_signals[t_idx, e_idx, :])\n",
    "        us_trial_resids[t_idx,e_idx,:] = us_trial_signals[t_idx, e_idx, :] - (intercept + slope * us_mean[e_idx,:])\n",
    "freqs, times, Zxx = scipy.signal.stft(us_trial_resids, fs=srate, window='hann', nperseg=winsize,\n",
    "                                      noverlap=overlap, nfft=int(winsize * 20), detrend='constant',\n",
    "                                      return_onesided=True, boundary='zeros', padded=True, axis=-1)\n",
    "us_layer_resid_psd = np.abs(Zxx) ** 2\n",
    "f_idx=(freqs>=freq_lims[0]) & (freqs<freq_lims[1])\n",
    "freqs=freqs[f_idx]\n",
    "us_layer_resid_psd=us_layer_resid_psd[:,:,f_idx,:]\n",
    "mus_layer_resid_psd=np.mean(us_layer_resid_psd,axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1aabb2",
   "metadata": {},
   "source": [
    "**Parameterize power spectra**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "157e3500",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n",
      "Running FOOOFGroup across 25 power spectra.\n",
      "\n",
      "FOOOF WARNING: Lower-bound peak width limit is < or ~= the frequency resolution: 0.50 <= 0.50\n",
      "\tLower bounds below frequency-resolution have no effect (effective lower bound is the frequency resolution).\n",
      "\tToo low a limit may lead to overfitting noise as small bandwidth peaks.\n",
      "\tWe recommend a lower bound of approximately 2x the frequency resolution.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ds_layer_resid_offsets=np.zeros((n_trials,n_electrodes))\n",
    "ds_layer_resid_slopes=np.zeros((n_trials,n_electrodes))\n",
    "ds_layer_resid_aperiodic=np.zeros((n_trials,n_electrodes,len(freqs)))\n",
    "for t in range(mds_layer_resid_psd.shape[0]):\n",
    "    trial_psd=mds_layer_resid_psd[t,:,:]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs,trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        ds_layer_resid_offsets[t,i]=fg[i].aperiodic_params[0]\n",
    "        ds_layer_resid_slopes[t,i]=fg[i].aperiodic_params[1]\n",
    "        ds_layer_resid_aperiodic[t,i,:]=gen_aperiodic(freqs,fg[i].aperiodic_params)\n",
    "\n",
    "us_layer_resid_offsets=np.zeros((n_trials,n_electrodes))\n",
    "us_layer_resid_slopes=np.zeros((n_trials,n_electrodes))\n",
    "us_layer_resid_aperiodic=np.zeros((n_trials,n_electrodes,len(freqs)))\n",
    "for t in range(mus_layer_resid_psd.shape[0]):\n",
    "    trial_psd=mus_layer_resid_psd[t,:,:]\n",
    "    \n",
    "    fg = FOOOFGroup()\n",
    "    fg.fit(freqs,trial_psd, freq_lims)\n",
    "    for i in range(trial_psd.shape[0]):\n",
    "        us_layer_resid_offsets[t,i]=fg[i].aperiodic_params[0]\n",
    "        us_layer_resid_slopes[t,i]=fg[i].aperiodic_params[1]\n",
    "        us_layer_resid_aperiodic[t,i,:]=gen_aperiodic(freqs,fg[i].aperiodic_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec64bbef",
   "metadata": {},
   "source": [
    "**Plot spectra and parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bededd35",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.size': 14,\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.top': False,\n",
    "    'xtick.major.size': 6,\n",
    "    'xtick.major.width': 1.2,\n",
    "    'ytick.major.size': 6,\n",
    "    'ytick.major.width': 1.2,\n",
    "    'legend.frameon': False,\n",
    "    'legend.handletextpad': 0.1,\n",
    "    'svg.fonttype': 'none',\n",
    "    'text.usetex': False\n",
    "})\n",
    "\n",
    "fig=plt.figure(figsize=(12,4))\n",
    "undetected_color=np.array([0,76.5,99.2])/100\n",
    "detected_color=np.array([98.4,54.1,1.2])/100\n",
    "\n",
    "gs = GridSpec(1, 4, width_ratios=[.275, .275, .225, .225])\n",
    "\n",
    "ax1=fig.add_subplot(gs[0])\n",
    "#m_detected=np.mean(ds_layer_aperiodic,axis=0)\n",
    "m_detected=np.log10(np.mean(mds_layer_resid_psd,axis=0))\n",
    "ax1.set_xscale('log')\n",
    "# ax1.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax1.plot(freqs,m_detected[i,:],color=detected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "# ax1.set_xlim([.1,125])\n",
    "ax1.set_xlabel('Frequency (Hz)')\n",
    "ax1.set_ylabel('Power')\n",
    "    \n",
    "ax2=fig.add_subplot(gs[1])\n",
    "# m_undetected=np.mean(us_layer_aperiodic,axis=0)\n",
    "m_undetected=np.log10(np.mean(mus_layer_resid_psd,axis=0))\n",
    "ax2.set_xscale('log')\n",
    "# ax2.set_yscale('log')\n",
    "for i in range(n_electrodes):\n",
    "    label=None\n",
    "    if i==5:\n",
    "        label='superficial'\n",
    "    if i==12:\n",
    "        label='middle'\n",
    "    if i==24:\n",
    "        label='deep'\n",
    "    ax2.plot(freqs,m_undetected[i,:],color=undetected_color,alpha=(i+1)/n_electrodes,label=label)\n",
    "plt.legend()\n",
    "#ax2.set_xlim([.1,125])\n",
    "ax2.set_xlabel('Frequency (Hz)')\n",
    "\n",
    "ax3=fig.add_subplot(gs[2])\n",
    "m_detected=np.mean(ds_layer_resid_offsets,axis=0)\n",
    "se_detected=np.std(ds_layer_resid_offsets,axis=0)\n",
    "ax3.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_resid_offsets,axis=0)\n",
    "se_undetected=np.std(us_layer_resid_offsets,axis=0)\n",
    "ax3.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax3.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax3.set_ylim([n_electrodes-1,0])\n",
    "ax3.set_ylabel('Virtual contact')\n",
    "ax3.set_xlabel('Offset')\n",
    "ax3.legend(loc='lower right')\n",
    "\n",
    "ax4=fig.add_subplot(gs[3])\n",
    "m_detected=np.mean(ds_layer_resid_slopes,axis=0)\n",
    "se_detected=np.std(ds_layer_resid_slopes,axis=0)\n",
    "ax4.plot(m_detected,np.arange(n_electrodes),\n",
    "         label='detected',\n",
    "         color=detected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_detected-se_detected,\n",
    "                  m_detected+se_detected,\n",
    "                  alpha=0.5,\n",
    "                  color=detected_color)\n",
    "\n",
    "m_undetected=np.mean(us_layer_resid_slopes,axis=0)\n",
    "se_undetected=np.std(us_layer_resid_slopes,axis=0)\n",
    "ax4.plot(m_undetected,\n",
    "         np.arange(n_electrodes),\n",
    "         label='undetected',\n",
    "         color=undetected_color)\n",
    "ax4.fill_betweenx(np.arange(n_electrodes),\n",
    "                  m_undetected-se_undetected,\n",
    "                  m_undetected+se_undetected,\n",
    "                  alpha=0.5,\n",
    "                  color=undetected_color)\n",
    "ax4.set_ylim([n_electrodes-1,0])\n",
    "ax4.set_xlabel('Slope')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('aan-e_i.svg')\n",
    "# plt.savefig('aan-e_i.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09146e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "m_detected=np.log10(np.mean(mds_layer_resid_psd,axis=0))\n",
    "m_undetected=np.log10(np.mean(mus_layer_resid_psd,axis=0))\n",
    "im=plt.imshow(m_detected-m_undetected,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Log Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41effcdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    ds_rel_power[:,i]=m_detected[:,i]/np.max(m_detected[:,i])\n",
    "im=plt.imshow(ds_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Detected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc8fd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "us_rel_power=np.zeros((n_electrodes,len(freqs)))\n",
    "for i in range(len(freqs)):\n",
    "    us_rel_power[:,i]=m_undetected[:,i]/np.max(m_undetected[:,i])\n",
    "im=plt.imshow(us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power (Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edb16923",
   "metadata": {},
   "outputs": [],
   "source": [
    "im=plt.imshow(ds_rel_power-us_rel_power,aspect='auto',extent=(freqs[0], freqs[-1], n_electrodes-1, 0))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Virtual contact')\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.set_label('Relative Power Difference\\n(Detected - Undetected)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164ea153",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a3c711-91ce-41f9-9c52-e4b19fb77ed6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285ded5a-cffa-4b4c-b110-22dee5873c67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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