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</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/forward/index.html">Forward models and source spaces</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/10_background_freesurfer.html">FreeSurfer MRI reconstruction</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/20_source_alignment.html">Source alignment and coordinate frames</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/25_automated_coreg.html">Using an automated approach to coregistration</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/30_forward.html">Head model and forward computation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/35_eeg_no_mri.html">EEG forward operator with a template MRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/50_background_freesurfer_mne.html">How MNE uses FreeSurfer’s outputs</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/80_fix_bem_in_blender.html">Fixing BEM and head surfaces</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/forward/90_compute_covariance.html">Computing a covariance matrix</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/inverse/index.html">Source localization and inverses</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/10_stc_class.html">The SourceEstimate data structure</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/20_dipole_fit.html">Source localization with equivalent current dipole (ECD) fit</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/30_mne_dspm_loreta.html">Source localization with MNE, dSPM, sLORETA, and eLORETA</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/35_dipole_orientations.html">The role of dipole orientations in distributed source localization</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/40_mne_fixed_free.html">Computing various MNE solutions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/50_beamformer_lcmv.html">Source reconstruction using an LCMV beamformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/60_visualize_stc.html">Visualize source time courses (stcs)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/70_eeg_mri_coords.html">EEG source localization given electrode locations on an MRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/80_brainstorm_phantom_elekta.html">Brainstorm Elekta phantom dataset tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/85_brainstorm_phantom_ctf.html">Brainstorm CTF phantom dataset tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/90_phantom_4DBTi.html">4D Neuroimaging/BTi phantom dataset tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/inverse/95_phantom_KIT.html">KIT phantom dataset tutorial</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/index.html">Statistical analysis of sensor data</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/10_background_stats.html">Statistical inference</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/20_erp_stats.html">Visualising statistical significance thresholds on EEG data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/40_cluster_1samp_time_freq.html">Non-parametric 1 sample cluster statistic on single trial power</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/50_cluster_between_time_freq.html">Non-parametric between conditions cluster statistic on single trial power</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/70_cluster_rmANOVA_time_freq.html">Mass-univariate twoway repeated measures ANOVA on single trial power</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-sensor-space/75_cluster_ftest_spatiotemporal.html">Spatiotemporal permutation F-test on full sensor data</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/stats-source-space/index.html">Statistical analysis of source estimates</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.html">Permutation t-test on source data with spatio-temporal clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-source-space/30_cluster_ftest_spatiotemporal.html">2 samples permutation test on source data with spatio-temporal clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/stats-source-space/60_cluster_rmANOVA_spatiotemporal.html">Repeated measures ANOVA on source data with spatio-temporal clustering</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/machine-learning/index.html">Machine learning models of neural activity</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/machine-learning/30_strf.html">Spectro-temporal receptive field (STRF) estimation on continuous data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/machine-learning/50_decoding.html">Decoding (MVPA)</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/clinical/index.html">Clinical applications</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/clinical/20_seeg.html">Working with sEEG data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/clinical/30_ecog.html">Working with ECoG data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/clinical/60_sleep.html">Sleep stage classification from polysomnography (PSG) data</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/simulation/index.html">Simulation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/simulation/10_array_objs.html">Creating MNE-Python data structures from scratch</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/simulation/70_point_spread.html">Corrupt known signal with point spread</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/simulation/80_dics.html">DICS for power mapping</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../../auto_tutorials/visualization/index.html">Visualization tutorials</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/visualization/10_publication_figure.html">Make figures more publication ready</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../auto_tutorials/visualization/20_ui_events.html">Using the event system to link figures</a></li>
</ul>
</details></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../index.html">Examples</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 has-children"><a class="reference internal" href="../io/index.html">Input/Output</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../io/elekta_epochs.html">Getting averaging info from .fif files</a></li>
<li class="toctree-l3"><a class="reference internal" href="../io/read_impedances.html">Getting impedances from raw files</a></li>
<li class="toctree-l3"><a class="reference internal" href="../io/read_neo_format.html">How to use data in neural ensemble (NEO) format</a></li>
<li class="toctree-l3"><a class="reference internal" href="../io/read_noise_covariance_matrix.html">Reading/Writing a noise covariance matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../io/read_xdf.html">Reading XDF EEG data</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../simulation/index.html">Data Simulation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../simulation/plot_stc_metrics.html">Compare simulated and estimated source activity</a></li>
<li class="toctree-l3"><a class="reference internal" href="../simulation/simulate_evoked_data.html">Generate simulated evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../simulation/simulate_raw_data.html">Generate simulated raw data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../simulation/simulated_raw_data_using_subject_anatomy.html">Simulate raw data using subject anatomy</a></li>
<li class="toctree-l3"><a class="reference internal" href="../simulation/source_simulator.html">Generate simulated source data</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../preprocessing/index.html">Preprocessing</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/contralateral_referencing.html">Using contralateral referencing for EEG</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/css.html">Cortical Signal Suppression (CSS) for removal of cortical signals</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/define_target_events.html">Define target events based on time lag, plot evoked response</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/eeg_bridging.html">Identify EEG Electrodes Bridged by too much Gel</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/eeg_csd.html">Transform EEG data using current source density (CSD)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/eog_artifact_histogram.html">Show EOG artifact timing</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/eog_regression.html">Reduce EOG artifacts through regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/epochs_metadata.html">Automated epochs metadata generation with variable time windows</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/esg_rm_heart_artefact_pcaobs.html">Principal Component Analysis - Optimal Basis Sets (PCA-OBS) removing cardiac artefact</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/find_ref_artifacts.html">Find MEG reference channel artifacts</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/fnirs_artifact_removal.html">Visualise NIRS artifact correction methods</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/ica_comparison.html">Compare the different ICA algorithms in MNE</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/interpolate_bad_channels.html">Interpolate bad channels for MEG/EEG channels</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/interpolate_to.html">Interpolate EEG data to any montage</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/movement_compensation.html">Maxwell filter data with movement compensation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/movement_detection.html">Annotate movement artifacts and reestimate dev_head_t</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/muscle_detection.html">Annotate muscle artifacts</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/muscle_ica.html">Removing muscle ICA components</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/otp.html">Plot sensor denoising using oversampled temporal projection</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/shift_evoked.html">Shifting time-scale in evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/virtual_evoked.html">Remap MEG channel types</a></li>
<li class="toctree-l3"><a class="reference internal" href="../preprocessing/xdawn_denoising.html">XDAWN Denoising</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../visualization/index.html">Visualization</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../visualization/3d_to_2d.html">How to convert 3D electrode positions to a 2D image</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/brain.html">Plotting with <code class="docutils literal notranslate"><span class="pre">mne.viz.Brain</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/channel_epochs_image.html">Visualize channel over epochs as an image</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/eeg_on_scalp.html">Plotting EEG sensors on the scalp</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/evoked_arrowmap.html">Plotting topographic arrowmaps of evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/evoked_topomap.html">Plotting topographic maps of evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/evoked_whitening.html">Whitening evoked data with a noise covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/eyetracking_plot_heatmap.html">Plotting eye-tracking heatmaps in MNE-Python</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/meg_sensors.html">Plotting sensor layouts of MEG systems</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/mne_helmet.html">Plot the MNE brain and helmet</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/montage_sgskip.html">Plotting sensor layouts of EEG systems</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/parcellation.html">Plot a cortical parcellation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/roi_erpimage_by_rt.html">Plot single trial activity, grouped by ROI and sorted by RT</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/ssp_projs_sensitivity_map.html">Sensitivity map of SSP projections</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/topo_compare_conditions.html">Compare evoked responses for different conditions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/topo_customized.html">Plot custom topographies for MEG sensors</a></li>
<li class="toctree-l3"><a class="reference internal" href="../visualization/xhemi.html">Cross-hemisphere comparison</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../time_frequency/index.html">Time-Frequency Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/compute_csd.html">Compute a cross-spectral density (CSD) matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/compute_source_psd_epochs.html">Compute Power Spectral Density of inverse solution from single epochs</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/source_label_time_frequency.html">Compute power and phase lock in label of the source space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/source_power_spectrum.html">Compute source power spectral density (PSD) in a label</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/source_power_spectrum_opm.html">Compute source power spectral density (PSD) of VectorView and OPM data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/source_space_time_frequency.html">Compute induced power in the source space with dSPM</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/temporal_whitening.html">Temporal whitening with AR model</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/time_frequency_erds.html">Compute and visualize ERDS maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/time_frequency_global_field_power.html">Explore event-related dynamics for specific frequency bands</a></li>
<li class="toctree-l3"><a class="reference internal" href="../time_frequency/time_frequency_simulated.html">Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../stats/index.html">Statistics Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../stats/cluster_stats_evoked.html">Permutation F-test on sensor data with 1D cluster level</a></li>
<li class="toctree-l3"><a class="reference internal" href="../stats/fdr_stats_evoked.html">FDR correction on T-test on sensor data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../stats/linear_regression_raw.html">Regression on continuous data (rER[P/F])</a></li>
<li class="toctree-l3"><a class="reference internal" href="../stats/sensor_permutation_test.html">Permutation T-test on sensor data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../stats/sensor_regression.html">Analysing continuous features with binning and regression in sensor space</a></li>
</ul>
</details></li>
<li class="toctree-l2 current active has-children"><a class="reference internal" href="index.html">Machine Learning (Decoding, Encoding, and MVPA)</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="decoding_csp_eeg.html">Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_csp_timefreq.html">Decoding in time-frequency space using Common Spatial Patterns (CSP)</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_rsa_sgskip.html">Representational Similarity Analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_spatio_temporal_source.html">Decoding source space data</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_spoc_CMC.html">Continuous Target Decoding with SPoC</a></li>
<li class="toctree-l3 current active"><a class="current reference internal" href="#">Decoding sensor space data with generalization across time and conditions</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_unsupervised_spatial_filter.html">Analysis of evoked response using ICA and PCA reduction techniques</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_xdawn_eeg.html">XDAWN Decoding From EEG data</a></li>
<li class="toctree-l3"><a class="reference internal" href="ems_filtering.html">Compute effect-matched-spatial filtering (EMS)</a></li>
<li class="toctree-l3"><a class="reference internal" href="linear_model_patterns.html">Linear classifier on sensor data with plot patterns and filters</a></li>
<li class="toctree-l3"><a class="reference internal" href="receptive_field_mtrf.html">Receptive Field Estimation and Prediction</a></li>
<li class="toctree-l3"><a class="reference internal" href="ssd_spatial_filters.html">Compute spatial filters with Spatio-Spectral Decomposition (SSD)</a></li>
</ul>
</details></li>
<li class="toctree-l2"><a class="reference internal" href="../connectivity/index.html">Connectivity Analysis Examples</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../forward/index.html">Forward modeling</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../forward/forward_sensitivity_maps.html">Display sensitivity maps for EEG and MEG sensors</a></li>
<li class="toctree-l3"><a class="reference internal" href="../forward/left_cerebellum_volume_source.html">Generate a left cerebellum volume source space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../forward/source_space_morphing.html">Use source space morphing</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../inverse/index.html">Inverse problem and source analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../inverse/compute_mne_inverse_epochs_in_label.html">Compute MNE-dSPM inverse solution on single epochs</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/compute_mne_inverse_raw_in_label.html">Compute sLORETA inverse solution on raw data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/compute_mne_inverse_volume.html">Compute MNE-dSPM inverse solution on evoked data in volume source space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/custom_inverse_solver.html">Source localization with a custom inverse solver</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/dics_epochs.html">Compute source level time-frequency timecourses using a DICS beamformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/dics_source_power.html">Compute source power using DICS beamformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/evoked_ers_source_power.html">Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/gamma_map_inverse.html">Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/label_activation_from_stc.html">Extracting time course from source_estimate object</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/label_from_stc.html">Generate a functional label from source estimates</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/label_source_activations.html">Extracting the time series of activations in a label</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/mixed_norm_inverse.html">Compute sparse inverse solution with mixed norm: MxNE and irMxNE</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/mixed_source_space_inverse.html">Compute MNE inverse solution on evoked data with a mixed source space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/mne_cov_power.html">Compute source power estimate by projecting the covariance with MNE</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/morph_surface_stc.html">Morph surface source estimate</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/morph_volume_stc.html">Morph volumetric source estimate</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/multi_dipole_model.html">Computing source timecourses with an XFit-like multi-dipole model</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/multidict_reweighted_tfmxne.html">Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/psf_ctf_label_leakage.html">Visualize source leakage among labels using a circular graph</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/psf_ctf_vertices.html">Plot point-spread functions (PSFs) and cross-talk functions (CTFs)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/psf_ctf_vertices_lcmv.html">Compute cross-talk functions for LCMV beamformers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/psf_volume.html">Plot point-spread functions (PSFs) for a volume</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/rap_music.html">Compute Rap-Music on evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/read_inverse.html">Reading an inverse operator</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/read_stc.html">Reading an STC file</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/resolution_metrics.html">Compute spatial resolution metrics in source space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/resolution_metrics_eegmeg.html">Compute spatial resolution metrics to compare MEG with EEG+MEG</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/snr_estimate.html">Estimate data SNR using an inverse</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/source_space_snr.html">Computing source space SNR</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/time_frequency_mixed_norm_inverse.html">Compute MxNE with time-frequency sparse prior</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/trap_music.html">Compute Trap-Music on evoked data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../inverse/vector_mne_solution.html">Plotting the full vector-valued MNE solution</a></li>
</ul>
</details></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../datasets/index.html">Examples on open datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../datasets/brainstorm_data.html">Brainstorm raw (median nerve) dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../datasets/hf_sef_data.html">HF-SEF dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../datasets/kernel_phantom.html">Kernel OPM phantom data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../datasets/limo_data.html">Single trial linear regression analysis with the LIMO dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../datasets/opm_data.html">Optically pumped magnetometer (OPM) data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../datasets/spm_faces_dataset.html">From raw data to dSPM on SPM Faces dataset</a></li>
</ul>
</details></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/glossary.html">Glossary</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/implementation.html">Implementation details</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/design_philosophy.html">Design philosophy</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/datasets.html">Example datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../generated/commands.html">Command-line tools</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../help/migrating.html">Migrating from other analysis software</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/cookbook.html">The typical M/EEG workflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/cite.html">How to cite MNE-Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../documentation/cited.html">Papers citing MNE-Python</a></li>
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<p class="admonition-title">Note</p>
<p><a class="reference internal" href="#sphx-glr-download-auto-examples-decoding-decoding-time-generalization-conditions-py"><span class="std std-ref">Go to the end</span></a>
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<section class="sphx-glr-example-title" id="decoding-sensor-space-data-with-generalization-across-time-and-conditions">
<span id="ex-linear-sensor-decoding"></span><span id="sphx-glr-auto-examples-decoding-decoding-time-generalization-conditions-py"></span><h1>Decoding sensor space data with generalization across time and conditions<a class="headerlink" href="#decoding-sensor-space-data-with-generalization-across-time-and-conditions" title="Link to this heading">#</a></h1>
<p>This example runs the analysis described in <a class="footnote-reference brackets" href="#footcite-kingdehaene2014" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>. It
illustrates how one can
fit a linear classifier to identify a discriminatory topography at a given time
instant and subsequently assess whether this linear model can accurately
predict all of the time samples of a second set of conditions.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Jean-Rémi King <jeanremi.king@gmail.com></span>
<span class="c1"># Alexandre Gramfort <alexandre.gramfort@inria.fr></span>
<span class="c1"># Denis Engemann <denis.engemann@gmail.com></span>
<span class="c1">#</span>
<span class="c1"># License: BSD-3-Clause</span>
<span class="c1"># Copyright the MNE-Python contributors.</span>
</pre></div>
</div>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a>
<span class="kn">import</span><span class="w"> </span><span class="nn">mne</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">mne.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">sample</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">mne.decoding</span><span class="w"> </span><span class="kn">import</span> <a href="../../generated/mne.decoding.GeneralizingEstimator.html#mne.decoding.GeneralizingEstimator" title="mne.decoding.GeneralizingEstimator" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">GeneralizingEstimator</span></a>
<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Preprocess data</span>
<a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_path</span></a> <span class="o">=</span> <a href="../../generated/mne.datasets.sample.data_path.html#mne.datasets.sample.data_path" title="mne.datasets.sample.data_path" class="sphx-glr-backref-module-mne-datasets-sample sphx-glr-backref-type-py-function"><span class="n">sample</span><span class="o">.</span><span class="n">data_path</span></a><span class="p">()</span>
<span class="c1"># Load and filter data, set up epochs</span>
<a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">meg_path</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_path</span></a> <span class="o">/</span> <span class="s2">"MEG"</span> <span class="o">/</span> <span class="s2">"sample"</span>
<a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw_fname</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">meg_path</span></a> <span class="o">/</span> <span class="s2">"sample_audvis_filt-0-40_raw.fif"</span>
<a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_fname</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">meg_path</span></a> <span class="o">/</span> <span class="s2">"sample_audvis_filt-0-40_raw-eve.fif"</span>
<a href="../../generated/mne.io.Raw.html#mne.io.Raw" title="mne.io.Raw" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw</span></a> <span class="o">=</span> <a href="../../generated/mne.io.read_raw_fif.html#mne.io.read_raw_fif" title="mne.io.read_raw_fif" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_fif</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw_fname</span></a><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">picks</span></a> <span class="o">=</span> <a href="../../generated/mne.pick_types.html#mne.pick_types" title="mne.pick_types" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">pick_types</span></a><span class="p">(</span><a href="../../generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw</span><span class="o">.</span><span class="n">info</span></a><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="s2">"bads"</span><span class="p">)</span> <span class="c1"># Pick MEG channels</span>
<a href="../../generated/mne.io.Raw.html#mne.io.Raw.filter" title="mne.io.Raw.filter" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-method"><span class="n">raw</span><span class="o">.</span><span class="n">filter</span></a><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">30.0</span><span class="p">,</span> <span class="n">fir_design</span><span class="o">=</span><span class="s2">"firwin"</span><span class="p">)</span> <span class="c1"># Band pass filtering signals</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a> <span class="o">=</span> <a href="../../generated/mne.read_events.html#mne.read_events" title="mne.read_events" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">read_events</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_fname</span></a><span class="p">)</span>
<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"Auditory/Left"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s2">"Auditory/Right"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s2">"Visual/Left"</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s2">"Visual/Right"</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
<span class="p">}</span>
<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.050</span>
<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="mf">0.400</span>
<span class="c1"># decimate to make the example faster to run, but then use verbose='error' in</span>
<span class="c1"># the Epochs constructor to suppress warning about decimation causing aliasing</span>
<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">decim</span></a> <span class="o">=</span> <span class="mi">2</span>
<a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a> <span class="o">=</span> <a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span></a><span class="p">(</span>
<a href="../../generated/mne.io.Raw.html#mne.io.Raw" title="mne.io.Raw" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw</span></a><span class="p">,</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">,</span>
<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a><span class="p">,</span>
<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span>
<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span>
<span class="n">proj</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">picks</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">picks</span></a><span class="p">,</span>
<span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">reject</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">mag</span><span class="o">=</span><span class="mf">5e-12</span><span class="p">),</span>
<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">decim</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">decim</span></a><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="s2">"error"</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 30 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 497 samples (3.310 s)
</pre></div>
</div>
<p>We will train the classifier on all left visual vs auditory trials
and test on all right visual vs auditory trials.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a><span class="p">(),</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"liblinear"</span><span class="p">),</span> <span class="c1"># liblinear is faster than lbfgs</span>
<span class="p">)</span>
<a href="../../generated/mne.decoding.GeneralizingEstimator.html#mne.decoding.GeneralizingEstimator" title="mne.decoding.GeneralizingEstimator" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_gen</span></a> <span class="o">=</span> <a href="../../generated/mne.decoding.GeneralizingEstimator.html#mne.decoding.GeneralizingEstimator" title="mne.decoding.GeneralizingEstimator" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">GeneralizingEstimator</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span></a><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s2">"roc_auc"</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Fit classifiers on the epochs where the stimulus was presented to the left.</span>
<span class="c1"># Note that the experimental condition y indicates auditory or visual</span>
<a href="../../generated/mne.decoding.GeneralizingEstimator.html#mne.decoding.GeneralizingEstimator.fit" title="mne.decoding.GeneralizingEstimator.fit" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-method"><span class="n">time_gen</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">X</span><span class="o">=</span><a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">[</span><span class="s2">"Left"</span><span class="p">]</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">[</span><span class="s2">"Left"</span><span class="p">]</span><span class="o">.</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">></span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> 0%| | Fitting GeneralizingEstimator : 0/35 [00:00<?, ?it/s]
3%|▎ | Fitting GeneralizingEstimator : 1/35 [00:00<00:01, 27.87it/s]
9%|▊ | Fitting GeneralizingEstimator : 3/35 [00:00<00:00, 43.50it/s]
11%|█▏ | Fitting GeneralizingEstimator : 4/35 [00:00<00:00, 38.74it/s]
17%|█▋ | Fitting GeneralizingEstimator : 6/35 [00:00<00:00, 44.21it/s]
23%|██▎ | Fitting GeneralizingEstimator : 8/35 [00:00<00:00, 47.52it/s]
31%|███▏ | Fitting GeneralizingEstimator : 11/35 [00:00<00:00, 55.29it/s]
40%|████ | Fitting GeneralizingEstimator : 14/35 [00:00<00:00, 60.61it/s]
49%|████▊ | Fitting GeneralizingEstimator : 17/35 [00:00<00:00, 64.79it/s]
54%|█████▍ | Fitting GeneralizingEstimator : 19/35 [00:00<00:00, 64.07it/s]
60%|██████ | Fitting GeneralizingEstimator : 21/35 [00:00<00:00, 63.07it/s]
69%|██████▊ | Fitting GeneralizingEstimator : 24/35 [00:00<00:00, 65.50it/s]
74%|███████▍ | Fitting GeneralizingEstimator : 26/35 [00:00<00:00, 64.84it/s]
83%|████████▎ | Fitting GeneralizingEstimator : 29/35 [00:00<00:00, 67.29it/s]
89%|████████▊ | Fitting GeneralizingEstimator : 31/35 [00:00<00:00, 66.53it/s]
97%|█████████▋| Fitting GeneralizingEstimator : 34/35 [00:00<00:00, 68.61it/s]
100%|██████████| Fitting GeneralizingEstimator : 35/35 [00:00<00:00, 66.70it/s]
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-1 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: #000;
--sklearn-color-text-muted: #666;
--sklearn-color-line: gray;
/* Definition of color scheme for unfitted estimators */
--sklearn-color-unfitted-level-0: #fff5e6;
--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
--sklearn-color-fitted-level-0: #f0f8ff;
--sklearn-color-fitted-level-1: #d4ebff;
--sklearn-color-fitted-level-2: #b3dbfd;
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/* Specific color for light theme */
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--sklearn-color-icon: #696969;
@media (prefers-color-scheme: dark) {
/* Redefinition of color scheme for dark theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
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--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-icon: #878787;
}
}
#sk-container-id-1 {
color: var(--sklearn-color-text);
}
#sk-container-id-1 pre {
padding: 0;
}
#sk-container-id-1 input.sk-hidden--visually {
border: 0;
clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
overflow: hidden;
padding: 0;
position: absolute;
width: 1px;
}
#sk-container-id-1 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
box-sizing: border-box;
padding-bottom: 0.4em;
background-color: var(--sklearn-color-background);
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#sk-container-id-1 div.sk-container {
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
}
#sk-container-id-1 div.sk-text-repr-fallback {
display: none;
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div.sk-parallel-item,
div.sk-serial,
div.sk-item {
/* draw centered vertical line to link estimators */
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
background-size: 2px 100%;
background-repeat: no-repeat;
background-position: center center;
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/* Parallel-specific style estimator block */
#sk-container-id-1 div.sk-parallel-item::after {
content: "";
width: 100%;
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
flex-grow: 1;
}
#sk-container-id-1 div.sk-parallel {
display: flex;
align-items: stretch;
justify-content: center;
background-color: var(--sklearn-color-background);
position: relative;
}
#sk-container-id-1 div.sk-parallel-item {
display: flex;
flex-direction: column;
}
#sk-container-id-1 div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
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#sk-container-id-1 div.sk-parallel-item:last-child::after {
align-self: flex-start;
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#sk-container-id-1 div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-container-id-1 div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-container-id-1 div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-container-id-1 label.sk-toggleable__label {
cursor: pointer;
display: flex;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
align-items: start;
justify-content: space-between;
gap: 0.5em;
}
#sk-container-id-1 label.sk-toggleable__label .caption {
font-size: 0.6rem;
font-weight: lighter;
color: var(--sklearn-color-text-muted);
}
#sk-container-id-1 label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-container-id-1 div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
text-align: left;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-1 div.sk-toggleable__content.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-1 div.sk-toggleable__content pre {
margin: 0.2em;
border-radius: 0.25em;
color: var(--sklearn-color-text);
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-1 div.sk-toggleable__content.fitted pre {
/* unfitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
/* Expand drop-down */
max-height: 200px;
max-width: 100%;
overflow: auto;
}
#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator-specific style */
/* Colorize estimator box */
#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
#sk-container-id-1 div.sk-label label.sk-toggleable__label,
#sk-container-id-1 div.sk-label label {
/* The background is the default theme color */
color: var(--sklearn-color-text-on-default-background);
}
/* On hover, darken the color of the background */
#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
/* Label box, darken color on hover, fitted */
#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator label */
#sk-container-id-1 div.sk-label label {
font-family: monospace;
font-weight: bold;
display: inline-block;
line-height: 1.2em;
}
#sk-container-id-1 div.sk-label-container {
text-align: center;
}
/* Estimator-specific */
#sk-container-id-1 div.sk-estimator {
font-family: monospace;
border: 1px dotted var(--sklearn-color-border-box);
border-radius: 0.25em;
box-sizing: border-box;
margin-bottom: 0.5em;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-1 div.sk-estimator.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
/* on hover */
#sk-container-id-1 div.sk-estimator:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-1 div.sk-estimator.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
/* Specification for estimator info (e.g. "i" and "?") */
/* Common style for "i" and "?" */
.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {
float: right;
font-size: smaller;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1em;
height: 1em;
width: 1em;
text-decoration: none !important;
margin-left: 0.5em;
text-align: center;
/* unfitted */
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
color: var(--sklearn-color-unfitted-level-1);
}
.sk-estimator-doc-link.fitted,
a:link.sk-estimator-doc-link.fitted,
a:visited.sk-estimator-doc-link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover,
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {
display: none;
z-index: 9999;
position: relative;
font-weight: normal;
right: .2ex;
padding: .5ex;
margin: .5ex;
width: min-content;
min-width: 20ex;
max-width: 50ex;
color: var(--sklearn-color-text);
box-shadow: 2pt 2pt 4pt #999;
/* unfitted */
background: var(--sklearn-color-unfitted-level-0);
border: .5pt solid var(--sklearn-color-unfitted-level-3);
}
.sk-estimator-doc-link.fitted span {
/* fitted */
background: var(--sklearn-color-fitted-level-0);
border: var(--sklearn-color-fitted-level-3);
}
.sk-estimator-doc-link:hover span {
display: block;
}
/* "?"-specific style due to the `<a>` HTML tag */
#sk-container-id-1 a.estimator_doc_link {
float: right;
font-size: 1rem;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1rem;
height: 1rem;
width: 1rem;
text-decoration: none;
/* unfitted */
color: var(--sklearn-color-unfitted-level-1);
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
}
#sk-container-id-1 a.estimator_doc_link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
#sk-container-id-1 a.estimator_doc_link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
#sk-container-id-1 a.estimator_doc_link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre><GeneralizingEstimator(base_estimator=Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression', LogisticRegression(solver='liblinear'))]), scoring='roc_auc', verbose=True, fitted with 35 estimators, fitted with 35 estimators></pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GeneralizingEstimator</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre><GeneralizingEstimator(base_estimator=Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression', LogisticRegression(solver='liblinear'))]), scoring='roc_auc', verbose=True, fitted with 35 estimators, fitted with 35 estimators></pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>base_estimator: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),
('logisticregression', LogisticRegression(solver='liblinear'))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(solver='liblinear')</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
</div>
<br />
<br /><p>Score on the epochs where the stimulus was presented to the right.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scores</span></a> <span class="o">=</span> <a href="../../generated/mne.decoding.GeneralizingEstimator.html#mne.decoding.GeneralizingEstimator.score" title="mne.decoding.GeneralizingEstimator.score" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-method"><span class="n">time_gen</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span>
<span class="n">X</span><span class="o">=</span><a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">[</span><span class="s2">"Right"</span><span class="p">]</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="../../generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">[</span><span class="s2">"Right"</span><span class="p">]</span><span class="o">.</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">></span> <span class="mi">2</span>
<span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> 0%| | Scoring GeneralizingEstimator : 0/1225 [00:00<?, ?it/s]
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95%|█████████▍| Scoring GeneralizingEstimator : 1161/1225 [00:03<00:00, 344.30it/s]
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98%|█████████▊| Scoring GeneralizingEstimator : 1197/1225 [00:03<00:00, 345.74it/s]
98%|█████████▊| Scoring GeneralizingEstimator : 1206/1225 [00:03<00:00, 341.72it/s]
99%|█████████▉| Scoring GeneralizingEstimator : 1217/1225 [00:03<00:00, 340.94it/s]
100%|██████████| Scoring GeneralizingEstimator : 1225/1225 [00:03<00:00, 342.83it/s]
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</pre></div>
</div>
<p>Plot</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" 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">subplots</span></a><span class="p">(</span><span class="n">layout</span><span class="o">=</span><span class="s2">"constrained"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/image_api.html#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage" class="sphx-glr-backref-module-matplotlib-image sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">im</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.matshow.html#matplotlib.axes.Axes.matshow" title="matplotlib.axes.Axes.matshow" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">matshow</span></a><span class="p">(</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scores</span></a><span class="p">,</span>
<span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">vmax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="s2">"RdBu_r"</span><span class="p">,</span>
<span class="n">origin</span><span class="o">=</span><span class="s2">"lower"</span><span class="p">,</span>
<span class="n">extent</span><span class="o">=</span><a href="../../generated/mne.Epochs.html#mne.Epochs.times" title="mne.Epochs.times" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-property"><span class="n">epochs</span><span class="o">.</span><span class="n">times</span></a><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]],</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.axhline.html#matplotlib.axes.Axes.axhline" title="matplotlib.axes.Axes.axhline" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">axhline</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.axvline.html#matplotlib.axes.Axes.axvline" title="matplotlib.axes.Axes.axvline" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axis.XAxis.set_ticks_position.html#matplotlib.axis.XAxis.set_ticks_position" title="matplotlib.axis.XAxis.set_ticks_position" class="sphx-glr-backref-module-matplotlib-axis sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span></a><span class="p">(</span><span class="s2">"bottom"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span>
<span class="s1">'Condition: "Right"</span><span class="se">\n</span><span class="s1">Testing Time (s)'</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">'Condition: "Left"</span><span class="se">\n</span><span class="s1">Training Time (s)'</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><span class="s2">"Generalization across time and condition"</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">"bold"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.colorbar.html#matplotlib.figure.Figure.colorbar" title="matplotlib.figure.Figure.colorbar" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/image_api.html#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage" class="sphx-glr-backref-module-matplotlib-image sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">im</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Performance (ROC AUC)"</span><span class="p">)</span>
<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>
</pre></div>
</div>
<img src="../../_images/sphx_glr_decoding_time_generalization_conditions_001.png" srcset="../../_images/sphx_glr_decoding_time_generalization_conditions_001.png" alt="Generalization across time and condition" class = "sphx-glr-single-img"/><section id="references">
<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
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<p>Jean-Rémi King and Stanislas Dehaene. Characterizing the dynamics of mental representations: the temporal generalization method. <em>Trends in Cognitive Sciences</em>, 18(4):203–210, 2014. <a class="reference external" href="https://doi.org/10.1016/j.tics.2014.01.002">doi:10.1016/j.tics.2014.01.002</a>.</p>
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