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Computation times#

72:16.586 total execution time for 198 files from all galleries:

Example

Time

Mem (MB)

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

01:32.045

541.5

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

01:27.939

731.2

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

01:15.050

247.1

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

01:14.647

935.6

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

01:13.441

2110.3

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

01:13.371

12.0

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

01:06.528

79.7

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

01:05.120

12.2

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

00:58.893

693.2

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

00:56.720

171.4

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

00:55.965

31.2

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

00:55.559

37.6

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

00:53.978

128.7

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

00:52.417

537.1

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

00:51.151

1455.9

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

00:47.704

384.6

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

00:47.652

839.2

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

00:46.857

9.2

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

00:46.187

1644.7

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

00:45.875

221.2

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

00:45.189

9.0

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

00:44.778

393.0

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

00:44.422

709.6

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

00:42.800

23.0

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

00:42.399

349.9

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

00:41.945

775.1

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

00:40.554

98.4

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

00:40.360

790.0

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

00:39.613

233.0

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

00:39.603

16.8

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

00:38.654

244.9

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

00:38.263

9.2

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

00:37.561

609.7

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

00:36.842

9.7

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

00:36.361

9.3

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

00:36.246

247.7

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

00:34.567

1540.1

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

00:34.390

134.8

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

00:33.369

558.3

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

00:33.032

75.0

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

00:32.779

48.6

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

00:32.545

1190.6

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

00:31.610

734.2

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

00:31.269

12.2

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

00:30.325

12.5

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

00:30.225

369.0

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

00:29.960

9.0

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

00:29.805

583.6

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

00:29.574

231.4

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

00:29.520

512.1

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

00:29.426

224.3

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

00:29.253

392.1

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

00:29.195

582.4

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

00:29.018

156.9

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

00:28.607

25.3

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

00:28.535

223.4

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

00:28.137

9.0

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

00:27.860

452.4

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

00:27.622

466.5

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

00:27.434

1005.5

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

00:27.381

619.7

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

00:27.116

558.2

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

00:27.080

233.9

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

00:26.706

557.5

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

00:26.453

407.3

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

00:25.302

13.9

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

00:25.103

199.0

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

00:24.901

626.5

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

00:24.689

181.6

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

00:24.530

844.6

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

00:24.302

69.9

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

00:23.721

249.7

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

00:23.697

163.6

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

00:23.668

155.6

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

00:23.537

47.6

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

00:23.080

277.3

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

00:22.603

39.3

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

00:22.542

105.2

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

00:22.461

838.4

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

00:22.418

9.7

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

00:21.632

251.3

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

00:21.406

290.4

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

00:21.094

44.9

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

00:20.912

163.6

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

00:20.405

180.9

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

00:20.328

641.0

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

00:20.230

9.0

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

00:20.081

841.5

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

00:19.452

12.8

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

00:19.323

81.2

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

00:18.906

233.5

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

00:18.906

9.2

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

00:18.752

363.7

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

00:18.709

97.4

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

00:18.552

196.0

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

00:18.543

9.2

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

00:17.653

9.2

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

00:17.597

211.7

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

00:17.468

39.2

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

00:17.365

533.3

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

00:17.254

49.0

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

00:17.244

9.7

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

00:17.142

89.7

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

00:16.753

659.0

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

00:16.548

162.0

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

00:16.512

21.5

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

00:16.129

68.2

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

00:16.094

208.8

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

00:15.996

341.7

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

00:15.804

112.4

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

00:15.801

99.9

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

00:15.677

25.0

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

00:15.227

12.2

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

00:15.169

176.9

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

00:14.927

275.3

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

00:14.898

558.3

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

00:14.336

172.3

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

00:14.235

9.6

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

00:13.839

27.5

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

00:13.725

437.7

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

00:12.730

170.7

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

00:12.729

165.9

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

00:12.519

26.8

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

00:12.424

128.6

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

00:12.308

9.0

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

00:12.215

382.4

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

00:12.210

267.9

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

00:12.156

9.0

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

00:12.109

233.8

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

00:12.078

213.0

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

00:12.066

216.5

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

00:11.792

296.9

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

00:11.362

248.2

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

00:11.203

209.2

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

00:11.061

25.7

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

00:10.902

157.5

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

00:10.393

23.9

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

00:10.359

9.0

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

00:10.041

83.2

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

00:09.717

491.1

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

00:09.418

30.2

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

00:09.210

113.9

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

00:09.032

393.7

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

00:08.870

91.3

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

00:08.854

22.2

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

00:08.581

244.6

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

00:08.224

128.6

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

00:08.110

113.7

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

00:07.971

351.8

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

00:07.778

151.9

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

00:07.742

63.3

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

00:07.589

199.5

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

00:07.528

51.9

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

00:07.516

9.0

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

00:07.466

128.6

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

00:07.462

275.1

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

00:07.190

171.2

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

00:07.137

82.9

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

00:06.936

9.0

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

00:06.883

9.0

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

00:06.717

596.5

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

00:06.689

1059.1

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

00:06.666

113.0

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

00:06.638

113.6

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

00:06.566

104.0

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

00:06.280

116.0

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

00:05.919

77.0

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

00:05.658

9.0

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

00:05.318

114.3

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

00:05.263

177.9

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

00:05.243

487.5

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

00:04.904

9.0

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

00:04.833

128.7

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

00:04.813

193.6

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

00:04.798

9.0

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

00:04.719

9.0

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

00:04.662

9.0

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

00:04.522

9.0

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

00:04.507

128.6

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

00:04.278

108.0

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

00:04.223

9.0

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

00:04.221

24.5

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

00:03.715

125.3

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

00:03.441

9.7

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

00:03.343

9.0

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

00:03.332

9.0

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

00:03.332

9.0

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

00:02.990

128.5

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

00:02.940

22.2

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

00:02.719

9.1

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

00:02.440

9.0

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

00:02.261

9.0

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

00:02.145

24.4

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

00:01.962

9.0

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

00:00.000

9.0

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

00:00.000

9.0

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