Introductory tutorials to MNE.
Introduction to Python
Configuring MNE python
Introduction to artifacts and artifact detection
Visualize Raw data
The Evoked data structure: evoked/averaged data
Visualize Epochs data
Artifact correction with Maxwell filter
Source alignment
The Info data structure
Modifying data in-place
Working with ECoG data
Artifact Correction with SSP
Filtering and resampling data
Computing covariance matrix
Epoching and averaging (ERP/ERF)
Rejecting bad data (channels and segments)
The Raw data structure: continuous data
The Epochs data structure: epoched data
Brainstorm CTF phantom tutorial dataset
Creating MNE’s data structures from scratch
Frequency and time-frequency sensors analysis
Head model and forward computation
Source localization with single dipole fit
Visualize Evoked data
Non-parametric 1 sample cluster statistic on single trial power
Brainstorm Elekta phantom tutorial dataset
Compute ICA on MEG data and remove artifacts
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
Source localization with MNE/dSPM/sLORETA
Corrupt known signal with point spread
Decoding sensor space data (MVPA)
Basic MEG and EEG data processing
The role of dipole orientations in distributed source localization
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
Spectro-temporal receptive field (STRF) estimation on continuous data
Background information on filtering
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