Computation times#
58:33.464 total execution time for 201 files from all galleries:
Example |
Time |
Mem (MB) |
---|---|---|
Visualize source time courses (stcs) ( |
01:22.453 |
0.0 |
Brainstorm Elekta phantom dataset tutorial ( |
01:04.236 |
0.0 |
EEG forward operator with a template MRI ( |
01:00.719 |
0.0 |
Compute source level time-frequency timecourses using a DICS beamformer ( |
00:57.733 |
0.0 |
Compute MNE inverse solution on evoked data with a mixed source space ( |
00:54.125 |
0.0 |
Repairing artifacts with ICA ( |
00:53.847 |
0.0 |
Head model and forward computation ( |
00:51.651 |
0.0 |
Getting started with mne.Report ( |
00:51.230 |
0.0 |
Source alignment and coordinate frames ( |
00:51.031 |
0.0 |
Compute source power spectral density (PSD) of VectorView and OPM data ( |
00:50.235 |
0.0 |
From raw data to dSPM on SPM Faces dataset ( |
00:49.986 |
0.0 |
Identify EEG Electrodes Bridged by too much Gel ( |
00:49.614 |
0.0 |
Repairing artifacts with SSP ( |
00:48.613 |
0.0 |
Plotting with mne.viz.Brain ( |
00:48.005 |
0.0 |
Source reconstruction using an LCMV beamformer ( |
00:47.335 |
0.0 |
Working with sEEG data ( |
00:46.214 |
0.0 |
Computing various MNE solutions ( |
00:44.683 |
0.0 |
Preprocessing optically pumped magnetometer (OPM) MEG data ( |
00:44.493 |
0.0 |
Working with CTF data: the Brainstorm auditory dataset ( |
00:43.754 |
0.0 |
Source localization with MNE, dSPM, sLORETA, and eLORETA ( |
00:43.628 |
0.0 |
Using an automated approach to coregistration ( |
00:41.803 |
0.0 |
Computing source timecourses with an XFit-like multi-dipole model ( |
00:41.421 |
0.0 |
Extracting and visualizing subject head movement ( |
00:40.987 |
0.0 |
Plotting the full vector-valued MNE solution ( |
00:40.284 |
0.0 |
EEG source localization given electrode locations on an MRI ( |
00:40.111 |
0.0 |
Overview of MEG/EEG analysis with MNE-Python ( |
00:38.990 |
0.0 |
Divide continuous data into equally-spaced epochs ( |
00:38.052 |
0.0 |
Kernel OPM phantom data ( |
00:37.106 |
0.0 |
The role of dipole orientations in distributed source localization ( |
00:36.880 |
0.0 |
Compute spatial resolution metrics to compare MEG with EEG+MEG ( |
00:35.679 |
0.0 |
Visualizing epoched data ( |
00:35.658 |
0.0 |
Visualizing Evoked data ( |
00:34.477 |
0.0 |
Compute source power estimate by projecting the covariance with MNE ( |
00:34.449 |
0.0 |
Source localization with equivalent current dipole (ECD) fit ( |
00:34.377 |
0.0 |
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method ( |
00:33.744 |
0.0 |
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM ( |
00:32.617 |
0.0 |
Brainstorm CTF phantom dataset tutorial ( |
00:32.600 |
0.0 |
Compute and visualize ERDS maps ( |
00:31.787 |
0.0 |
Simulate raw data using subject anatomy ( |
00:31.491 |
0.0 |
Compute power and phase lock in label of the source space ( |
00:31.169 |
0.0 |
Compute spatial resolution metrics in source space ( |
00:30.670 |
0.0 |
Filtering and resampling data ( |
00:29.306 |
0.0 |
Removing muscle ICA components ( |
00:29.161 |
0.0 |
Preprocessing functional near-infrared spectroscopy (fNIRS) data ( |
00:28.544 |
0.0 |
Plotting whitened data ( |
00:28.216 |
0.0 |
Plot point-spread functions (PSFs) for a volume ( |
00:28.004 |
0.0 |
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary ( |
00:27.251 |
0.0 |
Signal-space separation (SSS) and Maxwell filtering ( |
00:27.244 |
0.0 |
Compare the different ICA algorithms in MNE ( |
00:27.108 |
0.0 |
Use source space morphing ( |
00:26.735 |
0.0 |
Plot sensor denoising using oversampled temporal projection ( |
00:24.864 |
0.0 |
Sleep stage classification from polysomnography (PSG) data ( |
00:24.674 |
0.0 |
Plotting topographic maps of evoked data ( |
00:24.262 |
0.0 |
4D Neuroimaging/BTi phantom dataset tutorial ( |
00:23.795 |
0.0 |
Computing a covariance matrix ( |
00:23.636 |
0.0 |
Explore event-related dynamics for specific frequency bands ( |
00:23.510 |
0.0 |
Compute sparse inverse solution with mixed norm: MxNE and irMxNE ( |
00:23.507 |
0.0 |
Spatiotemporal permutation F-test on full sensor data ( |
00:23.444 |
0.0 |
Morph volumetric source estimate ( |
00:23.207 |
0.0 |
Overview of artifact detection ( |
00:22.591 |
0.0 |
Transform EEG data using current source density (CSD) ( |
00:22.186 |
0.0 |
Plotting topographic arrowmaps of evoked data ( |
00:21.814 |
0.0 |
Auto-generating Epochs metadata ( |
00:21.463 |
0.0 |
Continuous Target Decoding with SPoC ( |
00:21.322 |
0.0 |
Handling bad channels ( |
00:21.046 |
0.0 |
Visualize source leakage among labels using a circular graph ( |
00:20.962 |
0.0 |
KIT phantom dataset tutorial ( |
00:20.620 |
0.0 |
Optically pumped magnetometer (OPM) data ( |
00:20.610 |
0.0 |
Find MEG reference channel artifacts ( |
00:20.274 |
0.0 |
Compute a cross-spectral density (CSD) matrix ( |
00:19.851 |
0.0 |
Background information on filtering ( |
00:19.820 |
0.0 |
Frequency and time-frequency sensor analysis ( |
00:19.754 |
0.0 |
Compute MxNE with time-frequency sparse prior ( |
00:19.344 |
0.0 |
EEG analysis - Event-Related Potentials (ERPs) ( |
00:19.331 |
0.0 |
Decoding source space data ( |
00:18.281 |
0.0 |
Compute source power using DICS beamformer ( |
00:18.244 |
0.0 |
Decoding (MVPA) ( |
00:17.560 |
0.0 |
Setting the EEG reference ( |
00:17.421 |
0.0 |
Single trial linear regression analysis with the LIMO dataset ( |
00:17.019 |
0.0 |
Statistical inference ( |
00:16.741 |
0.0 |
Rejecting bad data spans and breaks ( |
00:16.677 |
0.0 |
Working with eye tracker data in MNE-Python ( |
00:16.492 |
0.0 |
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert) ( |
00:16.345 |
0.0 |
Computing source space SNR ( |
00:16.297 |
0.0 |
Repeated measures ANOVA on source data with spatio-temporal clustering ( |
00:16.058 |
0.0 |
How MNE uses FreeSurfer’s outputs ( |
00:15.899 |
0.0 |
Corrupt known signal with point spread ( |
00:15.629 |
0.0 |
DICS for power mapping ( |
00:15.601 |
0.0 |
Whitening evoked data with a noise covariance ( |
00:15.282 |
0.0 |
Plotting sensor layouts of MEG systems ( |
00:15.178 |
0.0 |
Decoding in time-frequency space using Common Spatial Patterns (CSP) ( |
00:14.886 |
0.0 |
Interpolate bad channels for MEG/EEG channels ( |
00:14.825 |
0.0 |
Using the event system to link figures ( |
00:13.704 |
0.0 |
Compute Rap-Music on evoked data ( |
00:13.688 |
0.0 |
Generate simulated raw data ( |
00:13.261 |
0.0 |
Exporting Epochs to Pandas DataFrames ( |
00:12.820 |
0.0 |
Compute cross-talk functions for LCMV beamformers ( |
00:12.789 |
0.0 |
Receptive Field Estimation and Prediction ( |
00:12.308 |
0.0 |
Repairing artifacts with regression ( |
00:12.121 |
0.0 |
Non-parametric 1 sample cluster statistic on single trial power ( |
00:11.989 |
0.0 |
The Spectrum and EpochsSpectrum classes: frequency-domain data ( |
00:11.928 |
0.0 |
Compute Trap-Music on evoked data ( |
00:11.856 |
0.0 |
Morph surface source estimate ( |
00:11.691 |
0.0 |
Cross-hemisphere comparison ( |
00:11.614 |
0.0 |
2 samples permutation test on source data with spatio-temporal clustering ( |
00:11.590 |
0.0 |
Generate a left cerebellum volume source space ( |
00:11.443 |
0.0 |
Spectro-temporal receptive field (STRF) estimation on continuous data ( |
00:11.028 |
0.0 |
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset ( |
00:10.802 |
0.0 |
Visualising statistical significance thresholds on EEG data ( |
00:10.757 |
0.0 |
Mass-univariate twoway repeated measures ANOVA on single trial power ( |
00:10.704 |
0.0 |
Brainstorm raw (median nerve) dataset ( |
00:10.650 |
0.0 |
Configuring MNE-Python ( |
00:10.426 |
0.0 |
Getting averaging info from .fif files ( |
00:10.402 |
0.0 |
Plot point-spread functions (PSFs) and cross-talk functions (CTFs) ( |
00:09.954 |
0.0 |
Permutation t-test on source data with spatio-temporal clustering ( |
00:09.762 |
0.0 |
Background on projectors and projections ( |
00:09.636 |
0.0 |
Annotate movement artifacts and reestimate dev_head_t ( |
00:09.497 |
0.0 |
Maxwell filter data with movement compensation ( |
00:09.098 |
0.0 |
Plotting eye-tracking heatmaps in MNE-Python ( |
00:09.072 |
0.0 |
Remap MEG channel types ( |
00:08.924 |
0.0 |
Display sensitivity maps for EEG and MEG sensors ( |
00:08.646 |
0.0 |
The Evoked data structure: evoked/averaged data ( |
00:08.638 |
0.0 |
Generate simulated evoked data ( |
00:08.523 |
0.0 |
Working with sensor locations ( |
00:08.516 |
0.0 |
Working with ECoG data ( |
00:08.281 |
0.0 |
The SourceEstimate data structure ( |
00:08.202 |
0.0 |
Reduce EOG artifacts through regression ( |
00:07.980 |
0.0 |
How to convert 3D electrode positions to a 2D image ( |
00:07.894 |
0.0 |
Compare simulated and estimated source activity ( |
00:07.582 |
0.0 |
Built-in plotting methods for Raw objects ( |
00:06.947 |
0.0 |
Regression-based baseline correction ( |
00:06.883 |
0.0 |
Importing data from fNIRS devices ( |
00:06.860 |
0.0 |
Plot custom topographies for MEG sensors ( |
00:06.723 |
0.0 |
XDAWN Decoding From EEG data ( |
00:06.703 |
0.0 |
Working with Epoch metadata ( |
00:06.675 |
0.0 |
Principal Component Analysis - Optimal Basis Sets (PCA-OBS) removing cardiac artefact ( |
00:06.442 |
0.0 |
Make figures more publication ready ( |
00:06.412 |
0.0 |
Plot single trial activity, grouped by ROI and sorted by RT ( |
00:06.393 |
0.0 |
Decoding sensor space data with generalization across time and conditions ( |
00:06.246 |
0.0 |
Compute induced power in the source space with dSPM ( |
00:05.990 |
0.0 |
Fixing BEM and head surfaces ( |
00:05.955 |
0.0 |
Generate a functional label from source estimates ( |
00:05.953 |
0.0 |
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP) ( |
00:05.944 |
0.0 |
Plot the MNE brain and helmet ( |
00:05.918 |
0.0 |
Linear classifier on sensor data with plot patterns and filters ( |
00:05.872 |
0.0 |
Compute MNE-dSPM inverse solution on evoked data in volume source space ( |
00:05.745 |
0.0 |
The Epochs data structure: discontinuous data ( |
00:05.653 |
0.0 |
Visualise NIRS artifact correction methods ( |
00:05.569 |
0.0 |
Compute Power Spectral Density of inverse solution from single epochs ( |
00:05.473 |
0.0 |
Interpolate EEG data to any montage ( |
00:05.404 |
0.0 |
Plot a cortical parcellation ( |
00:05.288 |
0.0 |
Creating MNE-Python data structures from scratch ( |
00:05.221 |
0.0 |
Importing Data from Eyetracking devices ( |
00:05.080 |
0.0 |
Modifying data in-place ( |
00:05.045 |
0.0 |
Annotating continuous data ( |
00:04.926 |
0.0 |
Reading XDF EEG data ( |
00:04.918 |
0.0 |
Compute effect-matched-spatial filtering (EMS) ( |
00:04.755 |
0.0 |
XDAWN Denoising ( |
00:04.483 |
0.0 |
Generate simulated source data ( |
00:04.362 |
0.0 |
Parsing events from raw data ( |
00:04.346 |
0.0 |
Annotate muscle artifacts ( |
00:04.308 |
0.0 |
Sensitivity map of SSP projections ( |
00:04.084 |
0.0 |
Compute source power spectral density (PSD) in a label ( |
00:04.004 |
0.0 |
Analysing continuous features with binning and regression in sensor space ( |
00:04.001 |
0.0 |
Compute spatial filters with Spatio-Spectral Decomposition (SSD) ( |
00:03.788 |
0.0 |
Analysis of evoked response using ICA and PCA reduction techniques ( |
00:03.243 |
0.0 |
Visualize channel over epochs as an image ( |
00:03.108 |
0.0 |
Extracting the time series of activations in a label ( |
00:03.046 |
0.0 |
Source localization with a custom inverse solver ( |
00:02.990 |
0.0 |
Compare evoked responses for different conditions ( |
00:02.951 |
0.0 |
Compute MNE-dSPM inverse solution on single epochs ( |
00:02.810 |
0.0 |
Regression on continuous data (rER[P/F]) ( |
00:02.779 |
0.0 |
Estimate data SNR using an inverse ( |
00:02.514 |
0.0 |
Automated epochs metadata generation with variable time windows ( |
00:02.454 |
0.0 |
Reading an inverse operator ( |
00:02.328 |
0.0 |
Non-parametric between conditions cluster statistic on single trial power ( |
00:02.274 |
0.0 |
Working with events ( |
00:02.238 |
0.0 |
FreeSurfer MRI reconstruction ( |
00:01.992 |
0.0 |
Getting impedances from raw files ( |
00:01.843 |
0.0 |
Permutation T-test on sensor data ( |
00:01.781 |
0.0 |
The Raw data structure: continuous data ( |
00:01.672 |
0.0 |
Cortical Signal Suppression (CSS) for removal of cortical signals ( |
00:01.594 |
0.0 |
How to use data in neural ensemble (NEO) format ( |
00:01.565 |
0.0 |
Define target events based on time lag, plot evoked response ( |
00:01.455 |
0.0 |
Reading/Writing a noise covariance matrix ( |
00:01.408 |
0.0 |
Compute sLORETA inverse solution on raw data ( |
00:01.328 |
0.0 |
Temporal whitening with AR model ( |
00:01.281 |
0.0 |
Using contralateral referencing for EEG ( |
00:01.194 |
0.0 |
Plotting EEG sensors on the scalp ( |
00:01.044 |
0.0 |
HF-SEF dataset ( |
00:01.039 |
0.0 |
Permutation F-test on sensor data with 1D cluster level ( |
00:01.039 |
0.0 |
The Info data structure ( |
00:01.002 |
0.0 |
Extracting time course from source_estimate object ( |
00:00.937 |
0.0 |
Reading an STC file ( |
00:00.802 |
0.0 |
Shifting time-scale in evoked data ( |
00:00.707 |
0.0 |
FDR correction on T-test on sensor data ( |
00:00.588 |
0.0 |
Show EOG artifact timing ( |
00:00.488 |
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 |