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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/2b3354037141fa0033174f982871d566/plot_relative_positioning.zip b/dev/_downloads/2b3354037141fa0033174f982871d566/plot_relative_positioning.zip
diff --git a/dev/_downloads/36393305d195c3207520572a3b677a05/plot_basic_training_epochs.zip b/dev/_downloads/36393305d195c3207520572a3b677a05/plot_basic_training_epochs.zip
diff --git a/dev/_downloads/3d616473194ad5c360edcbb3e46c7a3a/plot_custom_dataset_example.zip b/dev/_downloads/3d616473194ad5c360edcbb3e46c7a3a/plot_custom_dataset_example.zip
diff --git a/dev/_downloads/497b39027218ebaf8ffa20c07a0eff8d/plot_split_dataset.zip b/dev/_downloads/497b39027218ebaf8ffa20c07a0eff8d/plot_split_dataset.zip
diff --git a/dev/_downloads/4a2751c5805e7a17be8ee78ee1f2d520/plot_sleep_staging_chambon2018.zip b/dev/_downloads/4a2751c5805e7a17be8ee78ee1f2d520/plot_sleep_staging_chambon2018.zip
diff --git a/dev/_downloads/52f5b6b37eb9b07c97027b1eb0fadcd4/plot_sleep_staging_usleep.zip b/dev/_downloads/52f5b6b37eb9b07c97027b1eb0fadcd4/plot_sleep_staging_usleep.zip
diff --git a/dev/_downloads/568f264ca0ecc135d137fd575c39cf8e/plot_regression.zip b/dev/_downloads/568f264ca0ecc135d137fd575c39cf8e/plot_regression.zip
diff --git a/...ads/5e85dd7b06a544f298bd1837eddfcbd5/plot_train_in_pure_pytorch_and_pytorch_lightning.zip b/...ads/5e85dd7b06a544f298bd1837eddfcbd5/plot_train_in_pure_pytorch_and_pytorch_lightning.zip
diff --git a/dev/_downloads/67cf91b203bc10a20a2f8703a04849f8/plot_data_augmentation_search.zip b/dev/_downloads/67cf91b203bc10a20a2f8703a04849f8/plot_data_augmentation_search.zip
diff --git a/dev/_downloads/6b30e1d8632bb747aae46f30c6993983/plot_bcic_iv_4_ecog_cropped.zip b/dev/_downloads/6b30e1d8632bb747aae46f30c6993983/plot_bcic_iv_4_ecog_cropped.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/701321eb74424104536be933ed2cbf9e/plot_moabb_dataset_example.zip b/dev/_downloads/701321eb74424104536be933ed2cbf9e/plot_moabb_dataset_example.zip
diff --git a/dev/_downloads/7ec8b4809f7c475073a9b5d8ebb962dc/plot_benchmark_preprocessing.zip b/dev/_downloads/7ec8b4809f7c475073a9b5d8ebb962dc/plot_benchmark_preprocessing.zip
diff --git a/dev/_downloads/975f746daaf2fbe1dfde86e12f8d5031/plot_how_train_test_and_tune.zip b/dev/_downloads/975f746daaf2fbe1dfde86e12f8d5031/plot_how_train_test_and_tune.zip
diff --git a/dev/_downloads/9f417c6133a57e14b27122d013590718/plot_mne_dataset_example.zip b/dev/_downloads/9f417c6133a57e14b27122d013590718/plot_mne_dataset_example.zip
diff --git a/dev/_downloads/add1a85fee267a1849d27de66d7d8a6b/plot_sleep_staging_eldele2021.zip b/dev/_downloads/add1a85fee267a1849d27de66d7d8a6b/plot_sleep_staging_eldele2021.zip
diff --git a/dev/_downloads/b5c94eb60270f5ff2f26d4d743e7c69d/plot_tuh_eeg_corpus.zip b/dev/_downloads/b5c94eb60270f5ff2f26d4d743e7c69d/plot_tuh_eeg_corpus.zip
diff --git a/dev/_downloads/c3317f5b839dfe85c3fa27a60420173e/plot_bcic_iv_2a_moabb_cropped.zip b/dev/_downloads/c3317f5b839dfe85c3fa27a60420173e/plot_bcic_iv_2a_moabb_cropped.zip
diff --git a/dev/_downloads/c58c35b5588d025b3e00c7e383793f93/benchmark_lazy_eager_loading.zip b/dev/_downloads/c58c35b5588d025b3e00c7e383793f93/benchmark_lazy_eager_loading.zip
diff --git a/dev/_downloads/cec1d60200f666e0fada2d86280a2abe/plot_tuh_discrete_multitarget.zip b/dev/_downloads/cec1d60200f666e0fada2d86280a2abe/plot_tuh_discrete_multitarget.zip
diff --git a/dev/_downloads/d323b5e4b5903c1466d0e7674a0bfab6/plot_bcic_iv_2a_moabb_trial.zip b/dev/_downloads/d323b5e4b5903c1466d0e7674a0bfab6/plot_bcic_iv_2a_moabb_trial.zip
diff --git a/dev/_downloads/e824ae71d652d80edc1e0be50f3fcab4/plot_data_augmentation.zip b/dev/_downloads/e824ae71d652d80edc1e0be50f3fcab4/plot_data_augmentation.zip
diff --git a/...wnloads/e920a22508db3ee26e946faad811f57f/plot_hyperparameter_tuning_with_scikit-learn.zip b/...wnloads/e920a22508db3ee26e946faad811f57f/plot_hyperparameter_tuning_with_scikit-learn.zip
diff --git a/dev/_downloads/f3c7c35609f54876319e72ff42b71bb9/plot_load_save_datasets.zip b/dev/_downloads/f3c7c35609f54876319e72ff42b71bb9/plot_load_save_datasets.zip
diff --git a/dev/_downloads/f56b29a9031a522f5bcdaa29212f6171/plot_bcic_iv_4_ecog_trial.zip b/dev/_downloads/f56b29a9031a522f5bcdaa29212f6171/plot_bcic_iv_4_ecog_trial.zip
diff --git a/dev/_images/sphx_glr_plot_benchmark_preprocessing_001.png b/dev/_images/sphx_glr_plot_benchmark_preprocessing_001.png
diff --git a/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png b/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png
diff --git a/dev/api.html b/dev/api.html
diff --git a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html
@@ -605,7 +605,7 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this
 <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;crop&quot;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f36c588cbe0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f1d9c8423e0&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -842,14 +842,14 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -16.9037     -8.3086        2.5378       20.7324  0.0006  0.5200
-      2    -13.7539     -7.1244        1.9596       18.0720  0.0006  0.4552
-      3    -12.7768     -6.7402        1.6787       17.1870  0.0005  0.4580
-      4    -11.7970     -6.3910        1.5638       16.4409  0.0004  0.4463
-      5    -11.2712     -6.1840        1.4237       16.0095  0.0002  0.4578
-      6    -10.5735     -5.9203        1.3549       15.4436  0.0001  0.4447
-      7     -9.8228     -5.6181        1.3132       14.7799  0.0000  0.4654
-      8     -9.0643     -5.3047        1.3316       14.0884  0.0000  0.4444
+      1    -16.9037     -8.3086        2.5378       20.7324  0.0006  0.4981
+      2    -13.7539     -7.1244        1.9596       18.0720  0.0006  0.4566
+      3    -12.7768     -6.7402        1.6787       17.1870  0.0005  0.4581
+      4    -11.7970     -6.3910        1.5638       16.4409  0.0004  0.4820
+      5    -11.2712     -6.1840        1.4237       16.0095  0.0002  0.4615
+      6    -10.5735     -5.9203        1.3549       15.4436  0.0001  0.4474
+      7     -9.8228     -5.6181        1.3132       14.7799  0.0000  0.4726
+      8     -9.0643     -5.3047        1.3316       14.0884  0.0000  0.4459
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -970,8 +970,8 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this h
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (2 minutes 41.540 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1347 MB</p>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (2 minutes 53.690 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1545 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.ipynb</span></code></a></p>

diff --git a/dev/auto_examples/advanced_training/plot_data_augmentation.html b/dev/auto_examples/advanced_training/plot_data_augmentation.html
@@ -599,7 +599,7 @@ <h3><a class="toc-backref" href="#id3" role="doc-backlink">Preprocessing</a><a c
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:69: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&quot;Preprocessing choices with lambda functions cannot be saved.&quot;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f35ff31caf0&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f1e5fd21060&gt;
 </pre></div>
 </div>
 </section>
@@ -793,10 +793,10 @@ <h3><a class="toc-backref" href="#id10" role="doc-backlink">Create an EEGClassif
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
-      1            0.2535        1.6458       0.2535            0.2535        5.1251  0.0006  1.7708
-      2            0.2639        1.3297       0.2500            0.2500        5.7606  0.0005  1.5905
-      3            0.2639        1.1539       0.2500            0.2500        5.1230  0.0002  1.5966
-      4            0.2708        1.1567       0.2535            0.2535        4.2554  0.0000  1.6232
+      1            0.2535        1.6458       0.2535            0.2535        5.1251  0.0006  1.7266
+      2            0.2639        1.3297       0.2500            0.2500        5.7606  0.0005  1.6148
+      3            0.2639        1.1539       0.2500            0.2500        5.1230  0.0002  1.6248
+      4            0.2708        1.1567       0.2535            0.2535        4.2554  0.0000  1.6185
 
 &lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
   module_=============================================================================================================================================
@@ -849,8 +849,8 @@ <h3><a class="toc-backref" href="#id12" role="doc-backlink">Setting the data aug
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a>
 </pre></div>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.714 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1128 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.129 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1006 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation.ipynb</span></code></a></p>

diff --git a/dev/auto_examples/advanced_training/plot_data_augmentation_search.html b/dev/auto_examples/advanced_training/plot_data_augmentation_search.html
@@ -620,7 +620,7 @@ <h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:69: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&quot;Preprocessing choices with lambda functions cannot be saved.&quot;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f35ff2c52d0&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f1e621107c0&gt;
 </pre></div>
 </div>
 </section>
@@ -1387,8 +1387,8 @@ <h3><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a cla
 Journal of Neural Engineering, 18(4), 046020.</p>
 </aside>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 32.905 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1163 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 32.486 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  920 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-search-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation_search.ipynb</span></code></a></p>