Introductory tutorials to MNE.
Introduction to Python
Introduction to artifacts and artifact detection
Visualize Raw data
The Evoked data structure: evoked/averaged data
Visualize Epochs data
Computing covariance matrix
Artifact correction with Maxwell filter
The Info data structure
Brainstorm Elekta phantom tutorial dataset
Artifact Correction with SSP
Modifying data in-place
Filtering and resampling data
Epoching and averaging (ERP/ERF)
Rejecting bad data (channels and segments)
Head model and forward computation
Decoding sensor space data
Brainstorm CTF phantom tutorial dataset
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
Visualize Evoked data
Source localization with single dipole fit
Compute ICA on MEG data and remove artifacts
Non-parametric 1 sample cluster statistic on single trial power
Source localization with MNE/dSPM/sLORETA
2 samples permutation test on source data with spatio-temporal clustering
EEG processing and Event Related Potentials (ERPs)
Non-parametric between conditions cluster statistic on single trial power
Corrupt known signal with point spread
Basic MEG and EEG data processing
Artifact Correction with ICA
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
Background information on filtering
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