Introductory tutorials to MNE.
Introduction to Python
Visualize Raw data
Introduction to artifacts and artifact detection
Visualize Epochs data
The Evoked data structure: evoked/averaged data
Computing covariance matrix
Artifact correction with Maxwell filter
The Info data structure
Modifying data in-place
Filtering and Resampling
Artifact Correction with SSP
Rejecting bad data (channels and segments)
Head model and forward computation
Epoching and averaging (ERP/ERF)
Visualize Evoked data
Decoding sensor space data
The Raw data structure: continuous data
The Epochs data structure: epoched data
Frequency and time-frequency sensors analysis
Creating MNE-Python’s data structures from scratch
Source localization with single dipole fit
Compute ICA on MEG data and remove artifacts
Source localization with MNE/dSPM/sLORETA
Artifact Correction with ICA
EEG processing and Event Related Potentials (ERPs)
2 samples permutation test on source data with spatio-temporal clustering
Basic MEG and EEG data processing
Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Spatiotemporal permutation F-test on full sensor data
Export epochs to Pandas DataFrame
Permutation t-test on toy data with spatial clustering
Permutation t-test on source data with spatio-temporal clustering
Mass-univariate twoway repeated measures ANOVA on single trial power
Repeated measures ANOVA on source data with spatio-temporal clustering
Brainstorm auditory tutorial dataset