Computation times#
56:08.024 total execution time for 201 files from all galleries:
Example |
Time |
Mem (MB) |
---|---|---|
Visualize source time courses (stcs) ( |
01:20.035 |
0.0 |
Brainstorm Elekta phantom dataset tutorial ( |
01:05.205 |
0.0 |
Source alignment and coordinate frames ( |
00:57.901 |
0.0 |
Compute source level time-frequency timecourses using a DICS beamformer ( |
00:54.818 |
0.0 |
EEG forward operator with a template MRI ( |
00:54.260 |
0.0 |
Source reconstruction using an LCMV beamformer ( |
00:53.229 |
0.0 |
Compute MNE inverse solution on evoked data with a mixed source space ( |
00:51.561 |
0.0 |
From raw data to dSPM on SPM Faces dataset ( |
00:50.896 |
0.0 |
Compute source power spectral density (PSD) of VectorView and OPM data ( |
00:49.542 |
0.0 |
Head model and forward computation ( |
00:49.370 |
0.0 |
Getting started with mne.Report ( |
00:48.876 |
0.0 |
Plotting with mne.viz.Brain ( |
00:47.107 |
0.0 |
Repairing artifacts with SSP ( |
00:46.200 |
0.0 |
Preprocessing optically pumped magnetometer (OPM) MEG data ( |
00:45.184 |
0.0 |
Repairing artifacts with ICA ( |
00:44.816 |
0.0 |
Using an automated approach to coregistration ( |
00:44.428 |
0.0 |
Working with sEEG data ( |
00:44.363 |
0.0 |
Computing various MNE solutions ( |
00:43.519 |
0.0 |
Source localization with MNE, dSPM, sLORETA, and eLORETA ( |
00:43.329 |
0.0 |
Working with CTF data: the Brainstorm auditory dataset ( |
00:42.392 |
0.0 |
Plotting the full vector-valued MNE solution ( |
00:39.726 |
0.0 |
Kernel OPM phantom data ( |
00:39.458 |
0.0 |
Overview of MEG/EEG analysis with MNE-Python ( |
00:38.707 |
0.0 |
Computing source timecourses with an XFit-like multi-dipole model ( |
00:37.884 |
0.0 |
The role of dipole orientations in distributed source localization ( |
00:36.852 |
0.0 |
Extracting and visualizing subject head movement ( |
00:36.817 |
0.0 |
EEG source localization given electrode locations on an MRI ( |
00:36.121 |
0.0 |
Compute spatial resolution metrics to compare MEG with EEG+MEG ( |
00:35.135 |
0.0 |
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method ( |
00:34.394 |
0.0 |
Compute source power estimate by projecting the covariance with MNE ( |
00:33.850 |
0.0 |
Source localization with equivalent current dipole (ECD) fit ( |
00:33.763 |
0.0 |
Compute spatial resolution metrics in source space ( |
00:33.146 |
0.0 |
Divide continuous data into equally-spaced epochs ( |
00:32.480 |
0.0 |
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM ( |
00:32.385 |
0.0 |
Filtering and resampling data ( |
00:32.014 |
0.0 |
Brainstorm CTF phantom dataset tutorial ( |
00:31.797 |
0.0 |
Visualizing Evoked data ( |
00:31.713 |
0.0 |
Visualizing epoched data ( |
00:31.357 |
0.0 |
Simulate raw data using subject anatomy ( |
00:30.356 |
0.0 |
Compute and visualize ERDS maps ( |
00:28.623 |
0.0 |
Compute power and phase lock in label of the source space ( |
00:28.450 |
0.0 |
Compare the different ICA algorithms in MNE ( |
00:27.540 |
0.0 |
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary ( |
00:27.383 |
0.0 |
Plot point-spread functions (PSFs) for a volume ( |
00:26.974 |
0.0 |
Preprocessing functional near-infrared spectroscopy (fNIRS) data ( |
00:26.556 |
0.0 |
Use source space morphing ( |
00:25.757 |
0.0 |
Explore event-related dynamics for specific frequency bands ( |
00:25.278 |
0.0 |
Plotting whitened data ( |
00:24.978 |
0.0 |
KIT phantom dataset tutorial ( |
00:24.955 |
0.0 |
Plot sensor denoising using oversampled temporal projection ( |
00:24.657 |
0.0 |
Plotting topographic maps of evoked data ( |
00:23.814 |
0.0 |
Overview of artifact detection ( |
00:23.568 |
0.0 |
Removing muscle ICA components ( |
00:23.271 |
0.0 |
Compute sparse inverse solution with mixed norm: MxNE and irMxNE ( |
00:23.239 |
0.0 |
Sleep stage classification from polysomnography (PSG) data ( |
00:23.068 |
0.0 |
Signal-space separation (SSS) and Maxwell filtering ( |
00:22.591 |
0.0 |
Plotting sensor layouts of MEG systems ( |
00:21.672 |
0.0 |
Background information on filtering ( |
00:21.631 |
0.0 |
Spatiotemporal permutation F-test on full sensor data ( |
00:21.502 |
0.0 |
Optically pumped magnetometer (OPM) data ( |
00:21.148 |
0.0 |
Visualize source leakage among labels using a circular graph ( |
00:21.035 |
0.0 |
Find MEG reference channel artifacts ( |
00:20.882 |
0.0 |
Computing a covariance matrix ( |
00:20.813 |
0.0 |
Morph volumetric source estimate ( |
00:20.667 |
0.0 |
Compute a cross-spectral density (CSD) matrix ( |
00:20.560 |
0.0 |
Plotting topographic arrowmaps of evoked data ( |
00:20.376 |
0.0 |
Transform EEG data using current source density (CSD) ( |
00:20.159 |
0.0 |
Working with eye tracker data in MNE-Python ( |
00:19.953 |
0.0 |
Auto-generating Epochs metadata ( |
00:19.788 |
0.0 |
Continuous Target Decoding with SPoC ( |
00:19.243 |
0.0 |
Compute MxNE with time-frequency sparse prior ( |
00:18.799 |
0.0 |
Frequency and time-frequency sensor analysis ( |
00:18.537 |
0.0 |
Compute source power using DICS beamformer ( |
00:18.493 |
0.0 |
4D Neuroimaging/BTi phantom dataset tutorial ( |
00:18.235 |
0.0 |
Identify EEG Electrodes Bridged by too much Gel ( |
00:18.162 |
0.0 |
Single trial linear regression analysis with the LIMO dataset ( |
00:17.954 |
0.0 |
Statistical inference ( |
00:17.898 |
0.0 |
Decoding source space data ( |
00:17.855 |
0.0 |
Decoding (MVPA) ( |
00:17.627 |
0.0 |
EEG analysis - Event-Related Potentials (ERPs) ( |
00:16.428 |
0.0 |
Whitening evoked data with a noise covariance ( |
00:15.950 |
0.0 |
Decoding in time-frequency space using Common Spatial Patterns (CSP) ( |
00:15.685 |
0.0 |
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert) ( |
00:15.479 |
0.0 |
Corrupt known signal with point spread ( |
00:15.435 |
0.0 |
Computing source space SNR ( |
00:15.082 |
0.0 |
How MNE uses FreeSurfer’s outputs ( |
00:14.769 |
0.0 |
Interpolate bad channels for MEG/EEG channels ( |
00:14.015 |
0.0 |
Using the event system to link figures ( |
00:13.989 |
0.0 |
The Spectrum and EpochsSpectrum classes: frequency-domain data ( |
00:13.892 |
0.0 |
Handling bad channels ( |
00:13.654 |
0.0 |
Repeated measures ANOVA on source data with spatio-temporal clustering ( |
00:13.598 |
0.0 |
DICS for power mapping ( |
00:13.422 |
0.0 |
Setting the EEG reference ( |
00:13.383 |
0.0 |
Compute cross-talk functions for LCMV beamformers ( |
00:12.954 |
0.0 |
Receptive Field Estimation and Prediction ( |
00:12.831 |
0.0 |
Exporting Epochs to Pandas DataFrames ( |
00:12.638 |
0.0 |
Spectro-temporal receptive field (STRF) estimation on continuous data ( |
00:12.402 |
0.0 |
Annotate movement artifacts and reestimate dev_head_t ( |
00:12.340 |
0.0 |
Compute Rap-Music on evoked data ( |
00:12.313 |
0.0 |
2 samples permutation test on source data with spatio-temporal clustering ( |
00:12.189 |
0.0 |
Plot the MNE brain and helmet ( |
00:12.143 |
0.0 |
Morph surface source estimate ( |
00:12.050 |
0.0 |
Generate simulated raw data ( |
00:11.988 |
0.0 |
Configuring MNE-Python ( |
00:11.425 |
0.0 |
Non-parametric 1 sample cluster statistic on single trial power ( |
00:11.312 |
0.0 |
Display sensitivity maps for EEG and MEG sensors ( |
00:11.056 |
0.0 |
Visualising statistical significance thresholds on EEG data ( |
00:10.768 |
0.0 |
Compute Trap-Music on evoked data ( |
00:10.508 |
0.0 |
Permutation t-test on source data with spatio-temporal clustering ( |
00:10.501 |
0.0 |
Plot point-spread functions (PSFs) and cross-talk functions (CTFs) ( |
00:10.428 |
0.0 |
Repairing artifacts with regression ( |
00:10.133 |
0.0 |
Reduce EOG artifacts through regression ( |
00:09.471 |
0.0 |
Getting averaging info from .fif files ( |
00:09.470 |
0.0 |
Generate simulated evoked data ( |
00:09.451 |
0.0 |
Working with ECoG data ( |
00:09.319 |
0.0 |
Generate a left cerebellum volume source space ( |
00:09.317 |
0.0 |
The Evoked data structure: evoked/averaged data ( |
00:09.223 |
0.0 |
Working with sensor locations ( |
00:09.154 |
0.0 |
Background on projectors and projections ( |
00:09.139 |
0.0 |
Mass-univariate twoway repeated measures ANOVA on single trial power ( |
00:09.040 |
0.0 |
Brainstorm raw (median nerve) dataset ( |
00:08.823 |
0.0 |
Remap MEG channel types ( |
00:08.638 |
0.0 |
How to convert 3D electrode positions to a 2D image ( |
00:08.540 |
0.0 |
Compare simulated and estimated source activity ( |
00:08.471 |
0.0 |
Plotting eye-tracking heatmaps in MNE-Python ( |
00:08.406 |
0.0 |
The SourceEstimate data structure ( |
00:08.352 |
0.0 |
Maxwell filter data with movement compensation ( |
00:08.025 |
0.0 |
Principal Component Analysis - Optimal Basis Sets (PCA-OBS) removing cardiac artefact ( |
00:07.820 |
0.0 |
Plot custom topographies for MEG sensors ( |
00:07.799 |
0.0 |
Rejecting bad data spans and breaks ( |
00:07.795 |
0.0 |
Regression-based baseline correction ( |
00:07.364 |
0.0 |
Importing data from fNIRS devices ( |
00:07.139 |
0.0 |
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset ( |
00:06.991 |
0.0 |
XDAWN Denoising ( |
00:06.701 |
0.0 |
Linear classifier on sensor data with plot patterns and filters ( |
00:06.693 |
0.0 |
Plot single trial activity, grouped by ROI and sorted by RT ( |
00:06.645 |
0.0 |
XDAWN Decoding From EEG data ( |
00:06.444 |
0.0 |
Built-in plotting methods for Raw objects ( |
00:06.076 |
0.0 |
Fixing BEM and head surfaces ( |
00:05.999 |
0.0 |
Plot a cortical parcellation ( |
00:05.917 |
0.0 |
Make figures more publication ready ( |
00:05.745 |
0.0 |
Decoding sensor space data with generalization across time and conditions ( |
00:05.723 |
0.0 |
Compute MNE-dSPM inverse solution on evoked data in volume source space ( |
00:05.655 |
0.0 |
Compute Power Spectral Density of inverse solution from single epochs ( |
00:05.527 |
0.0 |
Working with Epoch metadata ( |
00:05.521 |
0.0 |
Generate a functional label from source estimates ( |
00:05.423 |
0.0 |
Interpolate EEG data to any montage ( |
00:04.974 |
0.0 |
The Epochs data structure: discontinuous data ( |
00:04.880 |
0.0 |
Compute induced power in the source space with dSPM ( |
00:04.813 |
0.0 |
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP) ( |
00:04.794 |
0.0 |
Compute source power spectral density (PSD) in a label ( |
00:04.449 |
0.0 |
Sensitivity map of SSP projections ( |
00:04.311 |
0.0 |
Analysing continuous features with binning and regression in sensor space ( |
00:04.172 |
0.0 |
Cross-hemisphere comparison ( |
00:04.168 |
0.0 |
Reading XDF EEG data ( |
00:03.844 |
0.0 |
Compute effect-matched-spatial filtering (EMS) ( |
00:03.728 |
0.0 |
Compute spatial filters with Spatio-Spectral Decomposition (SSD) ( |
00:03.720 |
0.0 |
Generate simulated source data ( |
00:03.457 |
0.0 |
Visualise NIRS artifact correction methods ( |
00:03.391 |
0.0 |
Analysis of evoked response using ICA and PCA reduction techniques ( |
00:03.302 |
0.0 |
Modifying data in-place ( |
00:03.261 |
0.0 |
Regression on continuous data (rER[P/F]) ( |
00:03.231 |
0.0 |
Annotate muscle artifacts ( |
00:03.150 |
0.0 |
Source localization with a custom inverse solver ( |
00:03.122 |
0.0 |
Visualize channel over epochs as an image ( |
00:03.007 |
0.0 |
Permutation T-test on sensor data ( |
00:02.940 |
0.0 |
Compare evoked responses for different conditions ( |
00:02.857 |
0.0 |
Compute MNE-dSPM inverse solution on single epochs ( |
00:02.729 |
0.0 |
Automated epochs metadata generation with variable time windows ( |
00:02.710 |
0.0 |
Reading an inverse operator ( |
00:02.651 |
0.0 |
Extracting the time series of activations in a label ( |
00:02.630 |
0.0 |
Parsing events from raw data ( |
00:02.626 |
0.0 |
Importing Data from Eyetracking devices ( |
00:02.593 |
0.0 |
The Raw data structure: continuous data ( |
00:02.239 |
0.0 |
Creating MNE-Python data structures from scratch ( |
00:02.211 |
0.0 |
FreeSurfer MRI reconstruction ( |
00:02.151 |
0.0 |
Annotating continuous data ( |
00:02.012 |
0.0 |
Define target events based on time lag, plot evoked response ( |
00:01.970 |
0.0 |
Estimate data SNR using an inverse ( |
00:01.815 |
0.0 |
Non-parametric between conditions cluster statistic on single trial power ( |
00:01.792 |
0.0 |
Getting impedances from raw files ( |
00:01.690 |
0.0 |
Temporal whitening with AR model ( |
00:01.557 |
0.0 |
Working with events ( |
00:01.446 |
0.0 |
Cortical Signal Suppression (CSS) for removal of cortical signals ( |
00:01.441 |
0.0 |
Compute sLORETA inverse solution on raw data ( |
00:01.233 |
0.0 |
Reading/Writing a noise covariance matrix ( |
00:01.215 |
0.0 |
Plotting EEG sensors on the scalp ( |
00:01.208 |
0.0 |
Using contralateral referencing for EEG ( |
00:01.189 |
0.0 |
Shifting time-scale in evoked data ( |
00:01.140 |
0.0 |
HF-SEF dataset ( |
00:01.040 |
0.0 |
Permutation F-test on sensor data with 1D cluster level ( |
00:01.024 |
0.0 |
The Info data structure ( |
00:00.971 |
0.0 |
How to use data in neural ensemble (NEO) format ( |
00:00.920 |
0.0 |
Reading an STC file ( |
00:00.865 |
0.0 |
Extracting time course from source_estimate object ( |
00:00.827 |
0.0 |
Show EOG artifact timing ( |
00:00.737 |
0.0 |
FDR correction on T-test on sensor data ( |
00:00.574 |
0.0 |
Importing data from EEG devices ( |
00:00.000 |
0.0 |
Importing data from MEG devices ( |
00:00.000 |
0.0 |
Representational Similarity Analysis ( |
00:00.000 |
0.0 |
Plotting sensor layouts of EEG systems ( |
00:00.000 |
0.0 |