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Computation times#

68:08.694 total execution time for 198 files from all galleries:

Example

Time

Mem (MB)

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

01:23.016

725.9

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

01:03.167

12.0

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

01:00.037

2100.7

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

00:58.404

199.9

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

00:57.251

1363.3

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

00:57.244

567.8

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

00:55.335

112.1

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

00:53.605

12.6

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

00:53.186

170.2

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

00:52.728

233.7

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

00:50.749

207.4

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

00:49.860

695.4

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

00:49.168

167.5

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

00:47.935

778.2

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

00:46.642

913.1

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

00:46.366

701.9

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

00:44.561

235.1

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

00:43.692

274.0

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

00:42.429

9.0

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

00:41.954

135.0

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

00:41.150

736.9

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

00:40.590

334.3

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

00:39.712

218.6

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

00:38.560

1457.8

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

00:38.180

128.4

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

00:38.114

222.1

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

00:38.104

700.6

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

00:38.074

248.5

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

00:37.958

536.8

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

00:37.151

80.0

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

00:36.192

8.7

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

00:35.766

73.4

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

00:35.360

9.4

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

00:35.250

455.7

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

00:35.068

132.3

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

00:34.990

1221.8

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

00:34.756

24.0

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

00:34.606

1684.1

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

00:33.325

10.6

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

00:33.251

748.3

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

00:32.530

1397.1

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

00:31.295

437.7

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

00:30.941

558.6

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

00:30.672

8.8

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

00:30.635

178.3

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

00:30.375

584.5

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

00:29.794

137.0

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

00:29.507

835.1

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

00:29.493

437.2

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

00:29.368

609.7

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

00:29.254

25.4

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

00:29.192

648.7

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

00:29.144

330.4

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

00:28.833

571.4

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

00:28.650

298.4

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

00:28.463

377.7

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

00:28.089

69.2

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

00:27.251

100.5

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

00:27.215

90.4

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

00:27.085

467.8

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

00:26.937

9.0

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

00:26.787

318.3

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

00:26.754

139.4

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

00:25.921

99.7

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

00:25.900

9.2

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

00:25.885

293.4

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

00:25.767

576.3

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

00:25.179

486.3

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

00:24.762

238.5

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

00:24.197

711.0

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

00:23.768

46.2

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

00:23.219

330.3

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

00:23.174

15.5

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

00:22.950

219.9

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

00:22.746

112.1

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

00:22.664

15.7

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

00:22.302

137.0

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

00:21.925

156.6

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

00:21.773

579.0

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

00:21.441

836.7

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

00:21.376

678.6

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

00:20.970

556.7

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

00:20.448

8.8

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

00:20.404

8.7

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

00:20.242

308.4

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

00:19.870

71.7

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

00:19.821

111.0

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

00:19.600

541.4

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

00:19.391

26.5

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

00:18.894

26.1

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

00:18.433

8.8

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

00:18.433

160.0

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

00:18.309

163.4

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

00:17.966

21.2

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

00:17.770

191.5

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

00:16.960

10.6

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

00:16.840

9.4

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

00:16.751

905.9

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

00:16.493

8.7

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

00:16.485

24.9

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

00:16.444

112.2

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

00:15.896

9.4

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

00:15.813

125.0

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

00:15.597

142.5

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

00:15.539

205.1

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

00:15.452

27.2

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

00:15.418

136.8

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

00:15.336

1165.7

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

00:15.183

210.2

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

00:15.156

262.0

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

00:15.002

128.3

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

00:14.689

205.8

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

00:14.074

61.0

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

00:13.983

436.4

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

00:13.951

165.9

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

00:13.680

26.8

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

00:13.573

106.5

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

00:13.397

117.3

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

00:13.378

8.8

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

00:13.361

50.8

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

00:13.344

115.6

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

00:12.999

8.8

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

00:12.427

8.8

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

00:12.395

73.8

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

00:12.129

212.7

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

00:12.083

8.7

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

00:12.076

128.4

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

00:12.047

26.1

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

00:12.033

382.3

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

00:11.972

216.3

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

00:11.950

646.4

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

00:11.933

95.5

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

00:11.322

120.4

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

00:11.302

136.7

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

00:11.226

157.7

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

00:11.129

8.7

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

00:11.083

491.2

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

00:11.002

437.6

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

00:10.591

17.1

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

00:10.373

62.5

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

00:10.026

244.4

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

00:09.851

8.7

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

00:09.758

86.8

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

00:09.123

9.6

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

00:08.590

113.1

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

00:08.505

272.1

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

00:08.451

22.2

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

00:08.193

24.5

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

00:08.135

32.0

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

00:08.125

195.5

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

00:07.964

8.8

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

00:07.856

286.1

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

00:07.589

26.1

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

00:07.576

8.7

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

00:07.082

586.7

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

00:07.062

8.7

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

00:07.053

24.6

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

00:06.978

56.4

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

00:06.879

72.2

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

00:06.763

127.0

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

00:06.735

112.8

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

00:06.594

8.7

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

00:06.539

1033.6

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

00:06.538

199.7

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

00:06.455

8.7

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

00:06.182

8.7

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

00:06.004

8.7

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

00:05.812

8.7

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

00:05.727

77.0

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

00:05.713

8.9

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

00:05.643

25.4

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

00:05.478

487.2

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

00:05.283

9.4

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

00:05.025

128.2

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

00:05.001

149.1

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

00:04.894

8.7

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

00:04.778

8.7

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

00:04.771

73.6

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

00:04.664

21.4

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

00:04.581

9.0

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

00:04.551

8.7

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

00:04.081

49.7

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

00:03.635

8.7

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

00:03.584

9.4

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

00:03.468

8.7

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

00:03.338

226.9

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

00:03.237

8.7

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

00:03.144

8.7

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

00:02.937

22.3

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

00:02.565

8.7

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

00:02.371

8.7

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

00:02.315

8.9

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

00:02.268

24.4

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

00:02.114

8.7

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

00:00.000

8.7

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

00:00.000

8.7

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