Computation times

57:16.224 total execution time for 207 files from all galleries:

Example	Time	Mem (MB)
Identify EEG Electrodes Bridged by too much Gel (../examples/preprocessing/eeg_bridging.py)	01:20.691	0.0
Visualize source time courses (stcs) (../tutorials/inverse/60_visualize_stc.py)	01:14.917	0.0
Source reconstruction using an LCMV beamformer (../tutorials/inverse/50_beamformer_lcmv.py)	01:12.250	0.0
Quality control (QC) reports with mne.Report (../tutorials/preprocessing/14_quality_control_report.py)	01:09.552	0.0
Getting started with mne.Report (../tutorials/intro/70_report.py)	01:05.307	0.0
Plotting with mne.viz.Brain (../examples/visualization/brain.py)	01:04.099	0.0
Brainstorm Elekta phantom dataset tutorial (../tutorials/inverse/80_brainstorm_phantom_elekta.py)	01:03.602	0.0
Extracting and visualizing subject head movement (../tutorials/preprocessing/59_head_positions.py)	00:58.769	0.0
Working with sEEG data (../tutorials/clinical/20_seeg.py)	00:53.765	0.0
Compute source level time-frequency timecourses using a DICS beamformer (../examples/inverse/dics_epochs.py)	00:51.217	0.0
EEG forward operator with a template MRI (../tutorials/forward/35_eeg_no_mri.py)	00:48.787	0.0
Overview of MEG/EEG analysis with MNE-Python (../tutorials/intro/10_overview.py)	00:48.003	0.0
Source alignment and coordinate frames (../tutorials/forward/20_source_alignment.py)	00:47.783	0.0
Computing various MNE solutions (../tutorials/inverse/40_mne_fixed_free.py)	00:46.219	0.0
Repairing artifacts with SSP (../tutorials/preprocessing/50_artifact_correction_ssp.py)	00:45.941	0.0
Source localization with MNE, dSPM, sLORETA, and eLORETA (../tutorials/inverse/30_mne_dspm_loreta.py)	00:43.340	0.0
Working with CTF data: the Brainstorm auditory dataset (../tutorials/io/60_ctf_bst_auditory.py)	00:43.312	0.0
Compute source power spectral density (PSD) of VectorView and OPM data (../examples/time_frequency/source_power_spectrum_opm.py)	00:43.271	0.0
Compute MNE inverse solution on evoked data with a mixed source space (../examples/inverse/mixed_source_space_inverse.py)	00:42.272	0.0
Repairing artifacts with ICA (../tutorials/preprocessing/40_artifact_correction_ica.py)	00:41.380	0.0
Preprocessing optically pumped magnetometer (OPM) MEG data (../tutorials/preprocessing/80_opm_processing.py)	00:40.833	0.0
From raw data to dSPM on SPM Faces dataset (../examples/datasets/spm_faces_dataset.py)	00:40.147	0.0
Head model and forward computation (../tutorials/forward/30_forward.py)	00:39.950	0.0
Divide continuous data into equally-spaced epochs (../tutorials/epochs/60_make_fixed_length_epochs.py)	00:38.934	0.0
Plotting the full vector-valued MNE solution (../examples/inverse/vector_mne_solution.py)	00:38.326	0.0
Using an automated approach to coregistration (../tutorials/forward/25_automated_coreg.py)	00:37.781	0.0
Kernel OPM phantom data (../examples/datasets/kernel_phantom.py)	00:36.901	0.0
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM (../examples/inverse/evoked_ers_source_power.py)	00:36.201	0.0
Compute source power estimate by projecting the covariance with MNE (../examples/inverse/mne_cov_power.py)	00:35.793	0.0
The role of dipole orientations in distributed source localization (../tutorials/inverse/35_dipole_orientations.py)	00:35.505	0.0
Visualizing Evoked data (../tutorials/evoked/20_visualize_evoked.py)	00:33.439	0.0
Compute spatial resolution metrics to compare MEG with EEG+MEG (../examples/inverse/resolution_metrics_eegmeg.py)	00:33.354	0.0
Simulate raw data using subject anatomy (../examples/simulation/simulated_raw_data_using_subject_anatomy.py)	00:33.074	0.0
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method (../examples/inverse/gamma_map_inverse.py)	00:31.543	0.0
EEG source localization given electrode locations on an MRI (../tutorials/inverse/70_eeg_mri_coords.py)	00:31.108	0.0
Visualizing epoched data (../tutorials/epochs/20_visualize_epochs.py)	00:30.761	0.0
Compute power and phase lock in label of the source space (../examples/time_frequency/source_label_time_frequency.py)	00:30.185	0.0
Plotting whitened data (../tutorials/evoked/40_whitened.py)	00:30.097	0.0
Brainstorm CTF phantom dataset tutorial (../tutorials/inverse/85_brainstorm_phantom_ctf.py)	00:30.094	0.0
Compute spatial resolution metrics in source space (../examples/inverse/resolution_metrics.py)	00:30.033	0.0
Compute and visualize ERDS maps (../examples/time_frequency/time_frequency_erds.py)	00:28.804	0.0
Filtering and resampling data (../tutorials/preprocessing/30_filtering_resampling.py)	00:28.724	0.0
Signal-space separation (SSS) and Maxwell filtering (../tutorials/preprocessing/60_maxwell_filtering_sss.py)	00:27.189	0.0
Preprocessing functional near-infrared spectroscopy (fNIRS) data (../tutorials/preprocessing/70_fnirs_processing.py)	00:26.706	0.0
Plot point-spread functions (PSFs) for a volume (../examples/inverse/psf_volume.py)	00:26.560	0.0
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary (../examples/inverse/multidict_reweighted_tfmxne.py)	00:25.649	0.0
Plot sensor denoising using oversampled temporal projection (../examples/preprocessing/otp.py)	00:25.035	0.0
Explore event-related dynamics for specific frequency bands (../examples/time_frequency/time_frequency_global_field_power.py)	00:24.342	0.0
Compare the different ICA algorithms in MNE (../examples/preprocessing/ica_comparison.py)	00:23.365	0.0
Source localization with equivalent current dipole (ECD) fit (../tutorials/inverse/20_dipole_fit.py)	00:22.883	0.0
Overview of artifact detection (../tutorials/preprocessing/10_preprocessing_overview.py)	00:22.864	0.0
KIT phantom dataset tutorial (../tutorials/inverse/95_phantom_KIT.py)	00:22.562	0.0
Plotting topographic maps of evoked data (../examples/visualization/evoked_topomap.py)	00:22.183	0.0
Computing a covariance matrix (../tutorials/forward/90_compute_covariance.py)	00:21.945	0.0
Visualize source leakage among labels using a circular graph (../examples/inverse/psf_ctf_label_leakage.py)	00:21.536	0.0
Background information on filtering (../tutorials/preprocessing/25_background_filtering.py)	00:21.436	0.0
Sleep stage classification from polysomnography (PSG) data (../tutorials/clinical/60_sleep.py)	00:21.049	0.0
Removing muscle ICA components (../examples/preprocessing/muscle_ica.py)	00:21.030	0.0
Compute sparse inverse solution with mixed norm: MxNE and irMxNE (../examples/inverse/mixed_norm_inverse.py)	00:20.569	0.0
Interpolate MEG or EEG data to any montage (../examples/preprocessing/interpolate_to.py)	00:20.469	0.0
Auto-generating Epochs metadata (../tutorials/epochs/40_autogenerate_metadata.py)	00:20.465	0.0
Computing source timecourses with an XFit-like multi-dipole model (../examples/inverse/multi_dipole_model.py)	00:20.068	0.0
Frequency and time-frequency sensor analysis (../tutorials/time-freq/20_sensors_time_frequency.py)	00:18.860	0.0
Compute source power using DICS beamformer (../examples/inverse/dics_source_power.py)	00:18.755	0.0
Transform EEG data using current source density (CSD) (../examples/preprocessing/eeg_csd.py)	00:18.597	0.0
Setting the EEG reference (../tutorials/preprocessing/55_setting_eeg_reference.py)	00:18.334	0.0
Use source space morphing (../examples/forward/source_space_morphing.py)	00:17.661	0.0
Advanced plotting customization by subclassing MNEBrowseFigure (../examples/visualization/onionskin.py)	00:17.377	0.0
Decoding source space data (../examples/decoding/decoding_spatio_temporal_source.py)	00:17.341	0.0
Compute MxNE with time-frequency sparse prior (../examples/inverse/time_frequency_mixed_norm_inverse.py)	00:17.252	0.0
Morph volumetric source estimate (../examples/inverse/morph_volume_stc.py)	00:17.145	0.0
Computing source space SNR (../examples/inverse/source_space_snr.py)	00:17.140	0.0
Compute a cross-spectral density (CSD) matrix (../examples/time_frequency/compute_csd.py)	00:16.893	0.0
Spectro-temporal receptive field (STRF) estimation on continuous data (../tutorials/machine-learning/30_strf.py)	00:16.815	0.0
Plotting topographic arrowmaps of evoked data (../examples/visualization/evoked_arrowmap.py)	00:16.760	0.0
Whitening evoked data with a noise covariance (../examples/visualization/evoked_whitening.py)	00:16.533	0.0
Continuous Target Decoding with SPoC (../examples/decoding/decoding_spoc_CMC.py)	00:16.341	0.0
Corrupt known signal with point spread (../tutorials/simulation/70_point_spread.py)	00:15.883	0.0
Working with eye tracker data in MNE-Python (../tutorials/preprocessing/90_eyetracking_data.py)	00:15.750	0.0
Statistical inference (../tutorials/stats-sensor-space/10_background_stats.py)	00:15.672	0.0
Spatiotemporal permutation F-test on full sensor data (../tutorials/stats-sensor-space/75_cluster_ftest_spatiotemporal.py)	00:15.604	0.0
Compute Rap-Music on evoked data (../examples/inverse/rap_music.py)	00:15.538	0.0
EEG analysis - Event-Related Potentials (ERPs) (../tutorials/evoked/30_eeg_erp.py)	00:15.366	0.0
Find MEG reference channel artifacts (../examples/preprocessing/find_ref_artifacts.py)	00:15.194	0.0
Cross-hemisphere comparison (../examples/visualization/xhemi.py)	00:15.183	0.0
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert) (../examples/time_frequency/time_frequency_simulated.py)	00:14.828	0.0
How MNE uses FreeSurfer’s outputs (../tutorials/forward/50_background_freesurfer_mne.py)	00:14.792	0.0
Exporting Epochs to Pandas DataFrames (../tutorials/epochs/50_epochs_to_data_frame.py)	00:14.746	0.0
Receptive Field Estimation and Prediction (../examples/decoding/receptive_field_mtrf.py)	00:14.713	0.0
Morph surface source estimate (../examples/inverse/morph_surface_stc.py)	00:14.489	0.0
Single trial linear regression analysis with the LIMO dataset (../examples/datasets/limo_data.py)	00:14.478	0.0
Optically pumped magnetometer (OPM) data (../examples/datasets/opm_data.py)	00:14.086	0.0
Repeated measures ANOVA on source data with spatio-temporal clustering (../tutorials/stats-source-space/60_cluster_rmANOVA_spatiotemporal.py)	00:13.793	0.0
Handling bad channels (../tutorials/preprocessing/15_handling_bad_channels.py)	00:13.721	0.0
Decoding (MVPA) (../tutorials/machine-learning/50_decoding.py)	00:13.570	0.0
Using the event system to link figures (../tutorials/visualization/20_ui_events.py)	00:13.295	0.0
Decoding in time-frequency space using Common Spatial Patterns (CSP) (../examples/decoding/decoding_csp_timefreq.py)	00:13.116	0.0
Compute cross-talk functions for LCMV beamformers (../examples/inverse/psf_ctf_vertices_lcmv.py)	00:13.035	0.0
DICS for power mapping (../tutorials/simulation/80_dics.py)	00:12.892	0.0
Plot the MNE brain and helmet (../examples/visualization/mne_helmet.py)	00:12.688	0.0
2 samples permutation test on source data with spatio-temporal clustering (../tutorials/stats-source-space/30_cluster_ftest_spatiotemporal.py)	00:12.544	0.0
4D Neuroimaging/BTi phantom dataset tutorial (../tutorials/inverse/90_phantom_4DBTi.py)	00:11.638	0.0
The Spectrum and EpochsSpectrum classes: frequency-domain data (../tutorials/time-freq/10_spectrum_class.py)	00:11.616	0.0
Interpolate bad channels for MEG/EEG channels (../examples/preprocessing/interpolate_bad_channels.py)	00:11.427	0.0
Display sensitivity maps for EEG and MEG sensors (../examples/forward/forward_sensitivity_maps.py)	00:10.710	0.0
Plot point-spread functions (PSFs) and cross-talk functions (CTFs) (../examples/inverse/psf_ctf_vertices.py)	00:10.633	0.0
Plot a cortical parcellation (../examples/visualization/parcellation.py)	00:10.515	0.0
Remap MEG channel types (../examples/preprocessing/virtual_evoked.py)	00:10.386	0.0
Plotting eye-tracking heatmaps in MNE-Python (../examples/visualization/eyetracking_plot_heatmap.py)	00:10.320	0.0
Getting averaging info from .fif files (../examples/io/elekta_epochs.py)	00:10.285	0.0
Generate a left cerebellum volume source space (../examples/forward/left_cerebellum_volume_source.py)	00:10.154	0.0
Permutation t-test on source data with spatio-temporal clustering (../tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.py)	00:10.133	0.0
Plotting sensor layouts of MEG systems (../examples/visualization/meg_sensors.py)	00:10.125	0.0
Generate simulated raw data (../examples/simulation/simulate_raw_data.py)	00:10.112	0.0
Compute Trap-Music on evoked data (../examples/inverse/trap_music.py)	00:09.920	0.0
Mass-univariate twoway repeated measures ANOVA on single trial power (../tutorials/stats-sensor-space/70_cluster_rmANOVA_time_freq.py)	00:09.785	0.0
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset (../tutorials/time-freq/50_ssvep.py)	00:09.694	0.0
Working with ECoG data (../tutorials/clinical/30_ecog.py)	00:09.404	0.0
Importing data from fNIRS devices (../tutorials/io/30_reading_fnirs_data.py)	00:09.293	0.0
Repairing artifacts with regression (../tutorials/preprocessing/35_artifact_correction_regression.py)	00:09.183	0.0
Working with sensor locations (../tutorials/intro/40_sensor_locations.py)	00:08.865	0.0
Background on projectors and projections (../tutorials/preprocessing/45_projectors_background.py)	00:08.687	0.0
Compare simulated and estimated source activity (../examples/simulation/plot_stc_metrics.py)	00:08.418	0.0
Brainstorm raw (median nerve) dataset (../examples/datasets/brainstorm_data.py)	00:08.416	0.0
The Evoked data structure: evoked/averaged data (../tutorials/evoked/10_evoked_overview.py)	00:08.409	0.0
The SourceEstimate data structure (../tutorials/inverse/10_stc_class.py)	00:08.349	0.0
Plot custom topographies for MEG sensors (../examples/visualization/topo_customized.py)	00:08.244	0.0
Reduce EOG artifacts through regression (../examples/preprocessing/eog_regression.py)	00:08.156	0.0
XDAWN Decoding From EEG data (../examples/decoding/decoding_xdawn_eeg.py)	00:07.923	0.0
Maxwell filter data with movement compensation (../examples/preprocessing/movement_compensation.py)	00:07.900	0.0
Annotate movement artifacts and reestimate dev_head_t (../examples/preprocessing/movement_detection.py)	00:07.776	0.0
How to convert 3D electrode positions to a 2D image (../examples/visualization/3d_to_2d.py)	00:07.715	0.0
Fixing BEM and head surfaces (../tutorials/forward/80_fix_bem_in_blender.py)	00:07.386	0.0
Generate simulated evoked data (../examples/simulation/simulate_evoked_data.py)	00:07.274	0.0
Principal Component Analysis - Optimal Basis Sets (PCA-OBS) removing cardiac artefact (../examples/preprocessing/esg_rm_heart_artefact_pcaobs.py)	00:07.008	0.0
Regression-based baseline correction (../tutorials/epochs/15_baseline_regression.py)	00:06.940	0.0
Plot single trial activity, grouped by ROI and sorted by RT (../examples/visualization/roi_erpimage_by_rt.py)	00:06.897	0.0
Rejecting bad data spans and breaks (../tutorials/preprocessing/20_rejecting_bad_data.py)	00:06.428	0.0
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP) (../examples/decoding/decoding_csp_eeg.py)	00:06.152	0.0
Generate a functional label from source estimates (../examples/inverse/label_from_stc.py)	00:05.910	0.0
Make figures more publication ready (../tutorials/visualization/10_publication_figure.py)	00:05.686	0.0
Working with Epoch metadata (../tutorials/epochs/30_epochs_metadata.py)	00:05.448	0.0
Linear classifier on sensor data with plot patterns and filters (../examples/decoding/linear_model_patterns.py)	00:05.391	0.0
Plotting EEG sensors on the scalp (../examples/visualization/eeg_on_scalp.py)	00:05.145	0.0
Visualising statistical significance thresholds on EEG data (../tutorials/stats-sensor-space/20_erp_stats.py)	00:05.145	0.0
The Epochs data structure: discontinuous data (../tutorials/epochs/10_epochs_overview.py)	00:05.021	0.0
Built-in plotting methods for Raw objects (../tutorials/raw/40_visualize_raw.py)	00:04.890	0.0
Compute Power Spectral Density of inverse solution from single epochs (../examples/time_frequency/compute_source_psd_epochs.py)	00:04.519	0.0
Compute source power spectral density (PSD) in a label (../examples/time_frequency/source_power_spectrum.py)	00:04.465	0.0
Sensitivity map of SSP projections (../examples/visualization/ssp_projs_sensitivity_map.py)	00:04.404	0.0
Compute spatial filters with Spatio-Spectral Decomposition (SSD) (../examples/decoding/ssd_spatial_filters.py)	00:04.213	0.0
Compute induced power in the source space with dSPM (../examples/time_frequency/source_space_time_frequency.py)	00:04.143	0.0
Reading XDF EEG data (../examples/io/read_xdf.py)	00:04.119	0.0
Modifying data in-place (../tutorials/intro/15_inplace.py)	00:04.118	0.0
Parsing events from raw data (../tutorials/intro/20_events_from_raw.py)	00:03.823	0.0
Non-parametric 1 sample cluster statistic on single trial power (../tutorials/stats-sensor-space/40_cluster_1samp_time_freq.py)	00:03.670	0.0
Removing the fMRI gradient artifact (../tutorials/preprocessing/58_fmri_gradient_artifact.py)	00:03.449	0.0
Analysing continuous features with binning and regression in sensor space (../examples/stats/sensor_regression.py)	00:03.281	0.0
Compute MNE-dSPM inverse solution on evoked data in volume source space (../examples/inverse/compute_mne_inverse_volume.py)	00:03.223	0.0
Compute effect-matched-spatial filtering (EMS) (../examples/decoding/ems_filtering.py)	00:03.218	0.0
Compare evoked responses for different conditions (../examples/visualization/topo_compare_conditions.py)	00:03.180	0.0
Visualize channel over epochs as an image (../examples/visualization/channel_epochs_image.py)	00:03.074	0.0
Generate simulated source data (../examples/simulation/source_simulator.py)	00:03.021	0.0
FreeSurfer MRI reconstruction (../tutorials/forward/10_background_freesurfer.py)	00:02.995	0.0
Source localization with a custom inverse solver (../examples/inverse/custom_inverse_solver.py)	00:02.994	0.0
Visualise NIRS artifact correction methods (../examples/preprocessing/fnirs_artifact_removal.py)	00:02.855	0.0
Decoding sensor space data with generalization across time and conditions (../examples/decoding/decoding_time_generalization_conditions.py)	00:02.787	0.0
Reading an inverse operator (../examples/inverse/read_inverse.py)	00:02.743	0.0
XDAWN Denoising (../examples/preprocessing/xdawn_denoising.py)	00:02.742	0.0
Annotate muscle artifacts (../examples/preprocessing/muscle_detection.py)	00:02.580	0.0
Integrating with R via rpy2 (../examples/stats/r_interop.py)	00:02.528	0.0
Compute MNE-dSPM inverse solution on single epochs (../examples/inverse/compute_mne_inverse_epochs_in_label.py)	00:02.528	0.0
Regression on continuous data (rER[P/F]) (../examples/stats/linear_regression_raw.py)	00:02.466	0.0
Importing Data from Eyetracking devices (../tutorials/io/70_reading_eyetracking_data.py)	00:02.462	0.0
Configuring MNE-Python (../tutorials/intro/50_configure_mne.py)	00:02.429	0.0
Getting impedances from raw files (../examples/io/read_impedances.py)	00:02.407	0.0
The Raw data structure: continuous data (../tutorials/raw/10_raw_overview.py)	00:02.304	0.0
Extracting the time series of activations in a label (../examples/inverse/label_source_activations.py)	00:02.284	0.0
Analysis of evoked response using ICA and PCA reduction techniques (../examples/decoding/decoding_unsupervised_spatial_filter.py)	00:02.263	0.0
Automated epochs metadata generation with variable time windows (../examples/preprocessing/epochs_metadata.py)	00:02.248	0.0
The Info data structure (../tutorials/intro/30_info.py)	00:02.218	0.0
Non-parametric between conditions cluster statistic on single trial power (../tutorials/stats-sensor-space/50_cluster_between_time_freq.py)	00:01.813	0.0
Permutation T-test on sensor data (../examples/stats/sensor_permutation_test.py)	00:01.643	0.0
Estimate data SNR using an inverse (../examples/inverse/snr_estimate.py)	00:01.591	0.0
Annotating continuous data (../tutorials/raw/30_annotate_raw.py)	00:01.458	0.0
Define target events based on time lag, plot evoked response (../examples/preprocessing/define_target_events.py)	00:01.457	0.0
Reading/Writing a noise covariance matrix (../examples/io/read_noise_covariance_matrix.py)	00:01.441	0.0
Temporal whitening with AR model (../examples/time_frequency/temporal_whitening.py)	00:01.416	0.0
Exploring epoch quality before rejection (../examples/preprocessing/plot_epoch_quality.py)	00:01.307	0.0
Cortical Signal Suppression (CSS) for removal of cortical signals (../examples/preprocessing/css.py)	00:01.244	0.0
Working with events (../tutorials/raw/20_event_arrays.py)	00:01.200	0.0
Creating MNE-Python data structures from scratch (../tutorials/simulation/10_array_objs.py)	00:01.186	0.0
Reading an STC file (../examples/inverse/read_stc.py)	00:01.182	0.0
Using contralateral referencing for EEG (../examples/preprocessing/contralateral_referencing.py)	00:01.100	0.0
Permutation F-test on sensor data with 1D cluster level (../examples/stats/cluster_stats_evoked.py)	00:01.020	0.0
HF-SEF dataset (../examples/datasets/hf_sef_data.py)	00:00.977	0.0
Compute sLORETA inverse solution on raw data (../examples/inverse/compute_mne_inverse_raw_in_label.py)	00:00.883	0.0
How to use data in neural ensemble (NEO) format (../examples/io/read_neo_format.py)	00:00.852	0.0
Shifting time-scale in evoked data (../examples/preprocessing/shift_evoked.py)	00:00.734	0.0
Extracting time course from source_estimate object (../examples/inverse/label_activation_from_stc.py)	00:00.661	0.0
Reading BCI2000 files (../examples/io/read_bci2k.py)	00:00.619	0.0
FDR correction on T-test on sensor data (../examples/stats/fdr_stats_evoked.py)	00:00.561	0.0
Show EOG artifact timing (../examples/preprocessing/eog_artifact_histogram.py)	00:00.461	0.0
Importing data from MEG devices (../tutorials/io/10_reading_meg_data.py)	00:00.000	0.0
Importing data from EEG devices (../tutorials/io/20_reading_eeg_data.py)	00:00.000	0.0
Representational Similarity Analysis (../examples/decoding/decoding_rsa_sgskip.py)	00:00.000	0.0
Plotting sensor layouts of EEG systems (../examples/visualization/montage_sgskip.py)	00:00.000	0.0