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‎docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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‎docs/_downloads/63b412797a45350b72c090ea95e36ee8/plot_Hinss2021_classification.py

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##############################################################################
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# Create util transformer
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n__subjects = 2
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title = "Datasets: "
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title = title + " " + dataset.code
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dataset.subject_list = dataset.subject_list[start_subject:stop_subject]
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dataset.subject_list = dataset.subject_list[:n__subjects]
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# Create Pipelines

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‎docs/_downloads/c0b6836dfec75ec67ce644b1417286cf/plot_Hinss2021_classification.ipynb

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"# Here we define the mne events for the RestingState paradigm.\nevents = dict(easy=2, diff=3)\n# The paradigm is adapted to the P300 paradigm.\nparadigm = RestingStateToP300Adapter(events=events, tmin=0, tmax=0.5)\n# We define a list with the dataset to use\ndatasets = [Hinss2021()]\n\n# To reduce the computation time in the example, we will only use the\n# first two subjects.\nstart_subject = 1\nstop_subject = 2\ntitle = \"Datasets: \"\nfor dataset in datasets:\n title = title + \" \" + dataset.code\n dataset.subject_list = dataset.subject_list[start_subject:stop_subject]"
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"# Here we define the mne events for the RestingState paradigm.\nevents = dict(easy=2, diff=3)\n# The paradigm is adapted to the P300 paradigm.\nparadigm = RestingStateToP300Adapter(events=events, tmin=0, tmax=0.5)\n# We define a list with the dataset to use\ndatasets = [Hinss2021()]\n\n# To reduce the computation time in the example, we will only use the\n# first two subjects.\nn__subjects = 2\ntitle = \"Datasets: \"\nfor dataset in datasets:\n title = title + \" \" + dataset.code\n dataset.subject_list = dataset.subject_list[:n__subjects]"
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‎docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html

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warn(f&quot;warnEpochs {epochs}&quot;)
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BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:14&lt;00:00, 4.94s/it]
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BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:14&lt;00:00, 4.96s/it]
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BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:15&lt;00:00, 5.00s/it]
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BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:15&lt;00:00, 5.03s/it]
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</pre></div>
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@@ -1023,8 +1021,8 @@ <h2>Combining Results<a class="headerlink" href="#combining-results" title="Perm
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<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" 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">show</span></a><span class="p">()</span>
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</pre></div>
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<img src="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" srcset="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" alt="Algorithm comparison" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 45.729 seconds)</p>
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<p><strong>Estimated memory usage:</strong> 365 MB</p>
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<img src="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" srcset="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" alt="Algorithm comparison" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 47.674 seconds)</p>
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<p><strong>Estimated memory usage:</strong> 191 MB</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py">
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<p><a class="reference download internal" download="" href="../../_downloads/3bb6d35e266c94de44c44ec75aa9d550/plot_mne_and_scikit_estimators.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mne_and_scikit_estimators.py</span></code></a></p>

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