Computation times#

63:28.729 total execution time for 198 files from all galleries:

Example

Time

Mem (MB)

Visualize source time courses (stcs) (../tutorials/inverse/60_visualize_stc.py)

01:11.168

675.1

Head model and forward computation (../tutorials/forward/30_forward.py)

01:02.246

215.7

Working with sEEG data (../tutorials/clinical/20_seeg.py)

00:58.685

594.3

Repairing artifacts with ICA (../tutorials/preprocessing/40_artifact_correction_ica.py)

00:55.825

12.2

From raw data to dSPM on SPM Faces dataset (../examples/datasets/spm_faces_dataset.py)

00:55.599

2099.8

Getting started with mne.Report (../tutorials/intro/70_report.py)

00:55.233

207.3

Repairing artifacts with SSP (../tutorials/preprocessing/50_artifact_correction_ssp.py)

00:54.857

31.8

Working with CTF data: the Brainstorm auditory dataset (../tutorials/io/60_ctf_bst_auditory.py)

00:52.158

644.7

Compute source level time-frequency timecourses using a DICS beamformer (../examples/inverse/dics_epochs.py)

00:51.673

1468.3

EEG forward operator with a template MRI (../tutorials/forward/35_eeg_no_mri.py)

00:50.021

660.9

Visualizing Evoked data (../tutorials/evoked/20_visualize_evoked.py)

00:49.470

34.6

Computing various MNE solutions (../tutorials/inverse/40_mne_fixed_free.py)

00:46.315

197.3

Plotting the full vector-valued MNE solution (../examples/inverse/vector_mne_solution.py)

00:45.894

192.0

Compute MNE inverse solution on evoked data with a mixed source space (../examples/inverse/mixed_source_space_inverse.py)

00:44.963

392.9

Brainstorm Elekta phantom dataset tutorial (../tutorials/inverse/80_brainstorm_phantom_elekta.py)

00:43.594

234.9

Simulate raw data using subject anatomy (../examples/simulation/simulated_raw_data_using_subject_anatomy.py)

00:43.274

794.8

Identify EEG Electrodes Bridged by too much Gel (../examples/preprocessing/eeg_bridging.py)

00:43.097

12.4

Source reconstruction using an LCMV beamformer (../tutorials/inverse/50_beamformer_lcmv.py)

00:42.310

934.3

Sleep stage classification from polysomnography (PSG) data (../tutorials/clinical/60_sleep.py)

00:41.576

1441.7

Overview of MEG/EEG analysis with MNE-Python (../tutorials/intro/10_overview.py)

00:41.245

780.3

Compute source power spectral density (PSD) of VectorView and OPM data (../examples/time_frequency/source_power_spectrum_opm.py)

00:40.561

384.6

Divide continuous data into equally-spaced epochs (../tutorials/epochs/60_make_fixed_length_epochs.py)

00:40.165

8.9

Compute spatial resolution metrics to compare MEG with EEG+MEG (../examples/inverse/resolution_metrics_eegmeg.py)

00:39.753

569.7

Compute spatial resolution metrics in source space (../examples/inverse/resolution_metrics.py)

00:38.107

448.1

Visualizing epoched data (../tutorials/epochs/20_visualize_epochs.py)

00:37.861

220.4

Source localization with MNE, dSPM, sLORETA, and eLORETA (../tutorials/inverse/30_mne_dspm_loreta.py)

00:37.191

160.5

Single trial linear regression analysis with the LIMO dataset (../examples/datasets/limo_data.py)

00:36.888

586.2

EEG analysis - Event-Related Potentials (ERPs) (../tutorials/evoked/30_eeg_erp.py)

00:36.683

9.6

Extracting and visualizing subject head movement (../tutorials/preprocessing/59_head_positions.py)

00:36.675

16.7

Background information on filtering (../tutorials/preprocessing/25_background_filtering.py)

00:34.866

9.2

Spatiotemporal permutation F-test on full sensor data (../tutorials/stats-sensor-space/75_cluster_ftest_spatiotemporal.py)

00:34.864

128.6

Kernel OPM phantom data (../examples/datasets/kernel_phantom.py)

00:34.075

1296.8

Source localization with equivalent current dipole (ECD) fit (../tutorials/inverse/20_dipole_fit.py)

00:33.719

204.9

Preprocessing functional near-infrared spectroscopy (fNIRS) data (../tutorials/preprocessing/70_fnirs_processing.py)

00:33.715

23.8

The role of dipole orientations in distributed source localization (../tutorials/inverse/35_dipole_orientations.py)

00:33.536

493.1

Plotting with mne.viz.Brain (../examples/visualization/brain.py)

00:32.747

24.0

Source alignment and coordinate frames (../tutorials/forward/20_source_alignment.py)

00:32.634

44.6

EEG source localization given electrode locations on an MRI (../tutorials/inverse/70_eeg_mri_coords.py)

00:32.597

532.5

Statistical inference (../tutorials/stats-sensor-space/10_background_stats.py)

00:32.003

9.1

How MNE uses FreeSurfer’s outputs (../tutorials/forward/50_background_freesurfer_mne.py)

00:30.668

24.8

Plotting topographic maps of evoked data (../examples/visualization/evoked_topomap.py)

00:30.290

8.9

Compute and visualize ERDS maps (../examples/time_frequency/time_frequency_erds.py)

00:30.198

84.7

Filtering and resampling data (../tutorials/preprocessing/30_filtering_resampling.py)

00:29.307

755.4

KIT phantom dataset tutorial (../tutorials/inverse/95_phantom_KIT.py)

00:28.840

1600.9

Frequency and time-frequency sensor analysis (../tutorials/time-freq/20_sensors_time_frequency.py)

00:28.099

8.9

Auto-generating Epochs metadata (../tutorials/epochs/40_autogenerate_metadata.py)

00:27.872

164.5

Removing muscle ICA components (../examples/preprocessing/muscle_ica.py)

00:27.535

12.2

Plotting whitened data (../tutorials/evoked/40_whitened.py)

00:27.037

12.4

Compute sparse inverse solution with mixed norm: MxNE and irMxNE (../examples/inverse/mixed_norm_inverse.py)

00:26.427

405.7

Computing a covariance matrix (../tutorials/forward/90_compute_covariance.py)

00:26.346

10.9

Computing source timecourses with an XFit-like multi-dipole model (../examples/inverse/multi_dipole_model.py)

00:26.262

609.9

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM (../examples/inverse/evoked_ers_source_power.py)

00:26.254

330.9

Compare the different ICA algorithms in MNE (../examples/preprocessing/ica_comparison.py)

00:25.733

9.0

Use source space morphing (../examples/forward/source_space_morphing.py)

00:25.609

592.9

Decoding (MVPA) (../tutorials/machine-learning/50_decoding.py)

00:25.235

130.2

Find MEG reference channel artifacts (../examples/preprocessing/find_ref_artifacts.py)

00:25.077

132.9

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method (../examples/inverse/gamma_map_inverse.py)

00:24.783

361.5

Spectro-temporal receptive field (STRF) estimation on continuous data (../tutorials/machine-learning/30_strf.py)

00:24.680

29.7

Using an automated approach to coregistration (../tutorials/forward/25_automated_coreg.py)

00:24.055

47.0

The Evoked data structure: evoked/averaged data (../tutorials/evoked/10_evoked_overview.py)

00:23.873

1005.4

Compute source power estimate by projecting the covariance with MNE (../examples/inverse/mne_cov_power.py)

00:23.747

134.6

Overview of artifact detection (../tutorials/preprocessing/10_preprocessing_overview.py)

00:23.635

467.2

Compute MxNE with time-frequency sparse prior (../examples/inverse/time_frequency_mixed_norm_inverse.py)

00:23.168

281.5

Working with eye tracker data in MNE-Python (../tutorials/preprocessing/90_eyetracking_data.py)

00:23.106

446.8

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary (../examples/inverse/multidict_reweighted_tfmxne.py)

00:22.859

372.4

Plotting topographic arrowmaps of evoked data (../examples/visualization/evoked_arrowmap.py)

00:22.826

9.0

Explore event-related dynamics for specific frequency bands (../examples/time_frequency/time_frequency_global_field_power.py)

00:22.618

882.5

Compute power and phase lock in label of the source space (../examples/time_frequency/source_label_time_frequency.py)

00:22.383

134.9

Transform EEG data using current source density (CSD) (../examples/preprocessing/eeg_csd.py)

00:22.072

235.1

Corrupt known signal with point spread (../tutorials/simulation/70_point_spread.py)

00:21.252

624.7

Compute a cross-spectral density (CSD) matrix (../examples/time_frequency/compute_csd.py)

00:21.243

237.4

The Epochs data structure: discontinuous data (../tutorials/epochs/10_epochs_overview.py)

00:21.049

155.6

Preprocessing optically pumped magnetometer (OPM) MEG data (../tutorials/preprocessing/80_opm_processing.py)

00:21.005

789.3

DICS for power mapping (../tutorials/simulation/80_dics.py)

00:20.972

298.1

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset (../tutorials/time-freq/50_ssvep.py)

00:20.489

8.9

The Spectrum and EpochsSpectrum classes: frequency-domain data (../tutorials/time-freq/10_spectrum_class.py)

00:20.070

104.2

Using the event system to link figures (../tutorials/visualization/20_ui_events.py)

00:19.535

26.3

Repeated measures ANOVA on source data with spatio-temporal clustering (../tutorials/stats-source-space/60_cluster_rmANOVA_spatiotemporal.py)

00:19.527

156.8

Receptive Field Estimation and Prediction (../examples/decoding/receptive_field_mtrf.py)

00:19.524

9.0

Cross-hemisphere comparison (../examples/visualization/xhemi.py)

00:19.514

19.5

Optically pumped magnetometer (OPM) data (../examples/datasets/opm_data.py)

00:19.318

874.8

Morph volumetric source estimate (../examples/inverse/morph_volume_stc.py)

00:18.773

853.3

Visualize source leakage among labels using a circular graph (../examples/inverse/psf_ctf_label_leakage.py)

00:18.625

582.6

Brainstorm CTF phantom dataset tutorial (../tutorials/inverse/85_brainstorm_phantom_ctf.py)

00:18.443

557.1

Handling bad channels (../tutorials/preprocessing/15_handling_bad_channels.py)

00:18.231

54.7

Compare simulated and estimated source activity (../examples/simulation/plot_stc_metrics.py)

00:18.163

165.6

Decoding source space data (../examples/decoding/decoding_spatio_temporal_source.py)

00:17.707

311.3

Make figures more publication ready (../tutorials/visualization/10_publication_figure.py)

00:17.359

21.5

Compute source power using DICS beamformer (../examples/inverse/dics_source_power.py)

00:17.110

151.6

Setting the EEG reference (../tutorials/preprocessing/55_setting_eeg_reference.py)

00:16.997

9.7

Importing data from fNIRS devices (../tutorials/io/30_reading_fnirs_data.py)

00:16.913

109.9

Rejecting bad data spans and breaks (../tutorials/preprocessing/20_rejecting_bad_data.py)

00:16.792

181.5

Plot sensor denoising using oversampled temporal projection (../examples/preprocessing/otp.py)

00:16.773

68.7

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert) (../examples/time_frequency/time_frequency_simulated.py)

00:16.739

49.0

Plot point-spread functions (PSFs) for a volume (../examples/inverse/psf_volume.py)

00:16.392

115.1

Morph surface source estimate (../examples/inverse/morph_surface_stc.py)

00:16.330

27.1

Mass-univariate twoway repeated measures ANOVA on single trial power (../tutorials/stats-sensor-space/70_cluster_rmANOVA_time_freq.py)

00:16.314

163.6

Background on projectors and projections (../tutorials/preprocessing/45_projectors_background.py)

00:16.094

9.2

Computing source space SNR (../examples/inverse/source_space_snr.py)

00:16.062

287.5

Interpolate bad channels for MEG/EEG channels (../examples/preprocessing/interpolate_bad_channels.py)

00:16.052

8.9

Signal-space separation (SSS) and Maxwell filtering (../tutorials/preprocessing/60_maxwell_filtering_sss.py)

00:15.920

12.8

Plotting sensor layouts of MEG systems (../examples/visualization/meg_sensors.py)

00:15.666

24.9

The Raw data structure: continuous data (../tutorials/raw/10_raw_overview.py)

00:15.642

112.3

Regression-based baseline correction (../tutorials/epochs/15_baseline_regression.py)

00:15.284

9.0

Exporting Epochs to Pandas DataFrames (../tutorials/epochs/50_epochs_to_data_frame.py)

00:15.096

414.5

Decoding in time-frequency space using Common Spatial Patterns (CSP) (../examples/decoding/decoding_csp_timefreq.py)

00:14.980

8.9

Permutation t-test on source data with spatio-temporal clustering (../tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.py)

00:14.085

113.6

Display sensitivity maps for EEG and MEG sensors (../examples/forward/forward_sensitivity_maps.py)

00:13.371

165.8

Continuous Target Decoding with SPoC (../examples/decoding/decoding_spoc_CMC.py)

00:13.343

310.3

Maxwell filter data with movement compensation (../examples/preprocessing/movement_compensation.py)

00:13.162

8.9

Compute Trap-Music on evoked data (../examples/inverse/trap_music.py)

00:13.130

227.4

Getting averaging info from .fif files (../examples/io/elekta_epochs.py)

00:13.107

8.9

Creating MNE-Python data structures from scratch (../tutorials/simulation/10_array_objs.py)

00:12.983

9.6

2 samples permutation test on source data with spatio-temporal clustering (../tutorials/stats-source-space/30_cluster_ftest_spatiotemporal.py)

00:12.839

188.2

Working with Epoch metadata (../tutorials/epochs/30_epochs_metadata.py)

00:12.839

9.6

The SourceEstimate data structure (../tutorials/inverse/10_stc_class.py)

00:12.771

26.7

Repairing artifacts with regression (../tutorials/preprocessing/35_artifact_correction_regression.py)

00:12.748

12.2

Generate simulated raw data (../examples/simulation/simulate_raw_data.py)

00:12.521

160.2

Linear classifier on sensor data with plot patterns and filters (../examples/decoding/linear_model_patterns.py)

00:12.466

1033.5

Fixing BEM and head surfaces (../tutorials/forward/80_fix_bem_in_blender.py)

00:12.415

157.6

Whitening evoked data with a noise covariance (../examples/visualization/evoked_whitening.py)

00:12.303

128.6

Compute Rap-Music on evoked data (../examples/inverse/rap_music.py)

00:12.127

137.0

Working with ECoG data (../tutorials/clinical/30_ecog.py)

00:12.117

658.9

Non-parametric 1 sample cluster statistic on single trial power (../tutorials/stats-sensor-space/40_cluster_1samp_time_freq.py)

00:11.837

9.1

Reduce EOG artifacts through regression (../examples/preprocessing/eog_regression.py)

00:11.796

248.2

4D Neuroimaging/BTi phantom dataset tutorial (../tutorials/inverse/90_phantom_4DBTi.py)

00:11.697

154.2

Remap MEG channel types (../examples/preprocessing/virtual_evoked.py)

00:11.496

8.9

Compute cross-talk functions for LCMV beamformers (../examples/inverse/psf_ctf_vertices_lcmv.py)

00:11.441

500.4

Working with sensor locations (../tutorials/intro/40_sensor_locations.py)

00:11.260

25.9

Plot custom topographies for MEG sensors (../examples/visualization/topo_customized.py)

00:11.240

155.5

Visualising statistical significance thresholds on EEG data (../tutorials/stats-sensor-space/20_erp_stats.py)

00:11.114

57.2

Generate a left cerebellum volume source space (../examples/forward/left_cerebellum_volume_source.py)

00:10.924

320.9

Generate simulated source data (../examples/simulation/source_simulator.py)

00:10.834

147.3

Annotate movement artifacts and reestimate dev_head_t (../examples/preprocessing/movement_detection.py)

00:10.812

382.5

Plot single trial activity, grouped by ROI and sorted by RT (../examples/visualization/roi_erpimage_by_rt.py)

00:10.196

8.9

Built-in plotting methods for Raw objects (../tutorials/raw/40_visualize_raw.py)

00:10.111

212.9

Generate simulated evoked data (../examples/simulation/simulate_evoked_data.py)

00:09.800

382.1

Plotting eye-tracking heatmaps in MNE-Python (../examples/visualization/eyetracking_plot_heatmap.py)

00:09.749

60.4

Compute Spectro-Spatial Decomposition (SSD) spatial filters (../examples/decoding/ssd_spatial_filters.py)

00:09.077

113.8

Plot point-spread functions (PSFs) and cross-talk functions (CTFs) (../examples/inverse/psf_ctf_vertices.py)

00:08.928

437.8

Automated epochs metadata generation with variable time windows (../examples/preprocessing/epochs_metadata.py)

00:08.812

244.6

Analysing continuous features with binning and regression in sensor space (../examples/stats/sensor_regression.py)

00:08.624

95.9

Brainstorm raw (median nerve) dataset (../examples/datasets/brainstorm_data.py)

00:08.441

490.8

Configuring MNE-Python (../tutorials/intro/50_configure_mne.py)

00:08.311

88.7

Compute induced power in the source space with dSPM (../examples/time_frequency/source_space_time_frequency.py)

00:08.144

593.3

Compute MNE-dSPM inverse solution on evoked data in volume source space (../examples/inverse/compute_mne_inverse_volume.py)

00:08.113

336.1

Annotating continuous data (../tutorials/raw/30_annotate_raw.py)

00:08.077

216.5

How to convert 3D electrode positions to a 2D image (../examples/visualization/3d_to_2d.py)

00:08.041

24.3

Plot a cortical parcellation (../examples/visualization/parcellation.py)

00:08.006

22.2

Decoding sensor space data with generalization across time and conditions (../examples/decoding/decoding_time_generalization_conditions.py)

00:07.880

128.5

XDAWN Denoising (../examples/preprocessing/xdawn_denoising.py)

00:07.395

128.5

Visualise NIRS artifact correction methods (../examples/preprocessing/fnirs_artifact_removal.py)

00:07.333

8.9

XDAWN Decoding From EEG data (../examples/decoding/decoding_xdawn_eeg.py)

00:07.179

128.6

Compute Power Spectral Density of inverse solution from single epochs (../examples/time_frequency/compute_source_psd_epochs.py)

00:07.077

26.2

Annotate muscle artifacts (../examples/preprocessing/muscle_detection.py)

00:07.069

195.7

Generate a functional label from source estimates (../examples/inverse/label_from_stc.py)

00:06.616

61.3

Plot the MNE brain and helmet (../examples/visualization/mne_helmet.py)

00:06.438

56.3

Extracting the time series of activations in a label (../examples/inverse/label_source_activations.py)

00:06.207

183.4

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP) (../examples/decoding/decoding_csp_eeg.py)

00:06.048

8.9

Compute source power spectral density (PSD) in a label (../examples/time_frequency/source_power_spectrum.py)

00:05.898

8.9

Reading XDF EEG data (../examples/io/read_xdf.py)

00:05.687

132.7

Source localization with a custom inverse solver (../examples/inverse/custom_inverse_solver.py)

00:05.655

165.0

Parsing events from raw data (../tutorials/intro/20_events_from_raw.py)

00:05.570

113.3

Working with events (../tutorials/raw/20_event_arrays.py)

00:05.530

113.0

Sensitivity map of SSP projections (../examples/visualization/ssp_projs_sensitivity_map.py)

00:05.527

50.9

Compute MNE-dSPM inverse solution on single epochs (../examples/inverse/compute_mne_inverse_epochs_in_label.py)

00:05.509

114.3

The Info data structure (../tutorials/intro/30_info.py)

00:05.357

8.9

Permutation T-test on sensor data (../examples/stats/sensor_permutation_test.py)

00:05.329

237.1

Non-parametric between conditions cluster statistic on single trial power (../tutorials/stats-sensor-space/50_cluster_between_time_freq.py)

00:05.325

9.1

HF-SEF dataset (../examples/datasets/hf_sef_data.py)

00:05.040

8.9

Analysis of evoked response using ICA and PCA reduction techniques (../examples/decoding/decoding_unsupervised_spatial_filter.py)

00:04.987

128.6

Compute effect-matched-spatial filtering (EMS) (../examples/decoding/ems_filtering.py)

00:04.678

128.5

Visualize channel over epochs as an image (../examples/visualization/channel_epochs_image.py)

00:04.661

9.2

FDR correction on T-test on sensor data (../examples/stats/fdr_stats_evoked.py)

00:04.648

8.9

Permutation F-test on sensor data with 1D cluster level (../examples/stats/cluster_stats_evoked.py)

00:04.352

8.9

Compare evoked responses for different conditions (../examples/visualization/topo_compare_conditions.py)

00:04.274

8.9

Reading an inverse operator (../examples/inverse/read_inverse.py)

00:04.186

24.4

Define target events based on time lag, plot evoked response (../examples/preprocessing/define_target_events.py)

00:04.144

8.9

Cortical Signal Suppression (CSS) for removal of cortical signals (../examples/preprocessing/css.py)

00:04.118

42.9

Regression on continuous data (rER[P/F]) (../examples/stats/linear_regression_raw.py)

00:03.585

26.1

Reading/Writing a noise covariance matrix (../examples/io/read_noise_covariance_matrix.py)

00:03.502

8.9

Importing Data from Eyetracking devices (../tutorials/io/70_reading_eyetracking_data.py)

00:03.488

75.0

Compute sLORETA inverse solution on raw data (../examples/inverse/compute_mne_inverse_raw_in_label.py)

00:03.343

8.9

Temporal whitening with AR model (../examples/time_frequency/temporal_whitening.py)

00:03.276

8.9

Using contralateral referencing for EEG (../examples/preprocessing/contralateral_referencing.py)

00:03.157

224.1

Show EOG artifact timing (../examples/preprocessing/eog_artifact_histogram.py)

00:03.098

128.5

Modifying data in-place (../tutorials/intro/15_inplace.py)

00:03.079

487.3

FreeSurfer MRI reconstruction (../tutorials/forward/10_background_freesurfer.py)

00:02.993

21.6

How to use data in neural ensemble (NEO) format (../examples/io/read_neo_format.py)

00:02.573

9.6

Plotting EEG sensors on the scalp (../examples/visualization/eeg_on_scalp.py)

00:02.379

24.4

Extracting time course from source_estimate object (../examples/inverse/label_activation_from_stc.py)

00:02.357

8.9

Reading an STC file (../examples/inverse/read_stc.py)

00:02.315

8.9

Estimate data SNR using an inverse (../examples/inverse/snr_estimate.py)

00:02.246

9.0

Shifting time-scale in evoked data (../examples/preprocessing/shift_evoked.py)

00:02.020

8.9

Importing data from MEG devices (../tutorials/io/10_reading_meg_data.py)

00:00.000

8.9

Importing data from EEG devices (../tutorials/io/20_reading_eeg_data.py)

00:00.000

8.9

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