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v0.17.1
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v0.17 (stable)
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Computation times
Computation times
¶
24:59.804
total execution time for
auto_tutorials
files:
03:27.001
:
Computing various MNE solutions
(plot_mne_solutions.py)
01:50.185
:
Source localization with MNE/dSPM/sLORETA/eLORETA
(plot_mne_dspm_source_localization.py)
01:08.867
:
DICS for power mapping
(plot_dics.py)
01:04.454
:
Repeated measures ANOVA on source data with spatio-temporal clustering
(plot_stats_cluster_spatio_temporal_repeated_measures_anova.py)
01:02.629
:
Brainstorm auditory tutorial dataset
(plot_brainstorm_auditory.py)
00:52.772
:
Corrupt known signal with point spread
(plot_point_spread.py)
00:52.026
:
Head model and forward computation
(plot_forward.py)
00:51.664
:
2 samples permutation test on source data with spatio-temporal clustering
(plot_stats_cluster_spatio_temporal_2samp.py)
00:46.344
:
The role of dipole orientations in distributed source localization
(plot_dipole_orientations.py)
00:42.143
:
Artifact Correction with ICA
(plot_artifacts_correction_ica.py)
00:40.333
:
Plotting whitened data
(plot_whitened.py)
00:38.187
:
Visualize Source time courses
(plot_visualize_stc.py)
00:36.916
:
Brainstorm Elekta phantom dataset tutorial
(plot_brainstorm_phantom_elekta.py)
00:33.521
:
Basic MEG and EEG data processing
(plot_introduction.py)
00:33.356
:
Source localization with equivalent current dipole (ECD) fit
(plot_dipole_fit.py)
00:32.208
:
Visualize Epochs data
(plot_visualize_epochs.py)
00:30.923
:
Computing a covariance matrix
(plot_compute_covariance.py)
00:26.638
:
Compute ICA on MEG data and remove artifacts
(plot_ica_from_raw.py)
00:24.204
:
Frequency and time-frequency sensors analysis
(plot_sensors_time_frequency.py)
00:24.174
:
Decoding (MVPA)
(plot_sensors_decoding.py)
00:23.956
:
Visualising statistical significance thresholds on EEG data
(plot_stats_cluster_erp.py)
00:21.637
:
Permutation t-test on source data with spatio-temporal clustering
(plot_stats_cluster_spatio_temporal.py)
00:21.383
:
EEG processing and Event Related Potentials (ERPs)
(plot_eeg_erp.py)
00:20.919
:
Visualize Raw data
(plot_visualize_raw.py)
00:20.664
:
Visualize Evoked data
(plot_visualize_evoked.py)
00:20.321
:
Spatiotemporal permutation F-test on full sensor data
(plot_stats_spatio_temporal_cluster_sensors.py)
00:19.354
:
Epoching and averaging (ERP/ERF)
(plot_epoching_and_averaging.py)
00:19.267
:
Source alignment and coordinate frames
(plot_source_alignment.py)
00:18.925
:
The SourceEstimate data structure
(plot_object_source_estimate.py)
00:17.634
:
Introduction to artifacts and artifact detection
(plot_artifacts_detection.py)
00:16.851
:
Working with ECoG data
(plot_ecog.py)
00:16.666
:
Brainstorm CTF phantom dataset tutorial
(plot_brainstorm_phantom_ctf.py)
00:16.648
:
Spectro-temporal receptive field (STRF) estimation on continuous data
(plot_receptive_field.py)
00:16.496
:
Statistical inference
(plot_background_statistics.py)
00:16.306
:
Background information on filtering
(plot_background_filtering.py)
00:12.549
:
Rejecting bad data (channels and segments)
(plot_artifacts_correction_rejection.py)
00:12.515
:
Artifact Correction with SSP
(plot_artifacts_correction_ssp.py)
00:12.499
:
The Epochs data structure: epoched data
(plot_object_epochs.py)
00:11.556
:
Mass-univariate twoway repeated measures ANOVA on single trial power
(plot_stats_cluster_time_frequency_repeated_measures_anova.py)
00:10.459
:
4D Neuroimaging/BTi phantom dataset tutorial
(plot_phantom_4DBTi.py)
00:09.240
:
Pandas querying and metadata with Epochs objects
(plot_metadata_epochs.py)
00:08.482
:
Artifact correction with Maxwell filter
(plot_artifacts_correction_maxwell_filtering.py)
00:08.308
:
The events and Annotations data structures
(plot_object_annotations.py)
00:07.258
:
The Info data structure
(plot_info.py)
00:06.359
:
The Raw data structure: continuous data
(plot_object_raw.py)
00:06.038
:
Filtering and resampling data
(plot_artifacts_correction_filtering.py)
00:05.017
:
The Evoked data structure: evoked/averaged data
(plot_object_evoked.py)
00:04.602
:
Modifying data in-place
(plot_modifying_data_inplace.py)
00:04.413
:
Creating MNE’s data structures from scratch
(plot_creating_data_structures.py)
00:03.786
:
Configuring MNE python
(plot_configuration.py)
00:03.190
:
Non-parametric between conditions cluster statistic on single trial power
(plot_stats_cluster_time_frequency.py)
00:02.828
:
Non-parametric 1 sample cluster statistic on single trial power
(plot_stats_cluster_1samp_test_time_frequency.py)
00:02.516
:
Export epochs to Pandas DataFrame
(plot_epochs_to_data_frame.py)
00:01.503
:
Introduction to Python
(plot_python_intro.py)
00:00.578
:
Morphing source estimates: Moving data from one brain to another
(plot_morph_stc.py)
00:00.535
:
FreeSurfer integration with MNE-Python
(plot_background_freesurfer.py)