On this page, I am collecting some resources that I created, mostly for data analysis of MEG and EEG data.
Source reconstruction of M/EEG data
If you would like to gain some experience with hands-on source reconstruction and best practices using MNE-Python, check out this material from the source reconstruction best practices workshop at CuttingEEGX in Nijmegen:
https://github.com/britta-wstnr/CuttingEEGX_Workshop
For the lab of Vitória Piai, I am creating a source reconstruction pipeline in FieldTrip. Not quite a tutorial, but with many explanatory comments in the text. Geared towards the data we handle in the lab, but I tried to
make it clear if something is Donders-specific, so it might still be a good place to get started. Work on this is mostly done, but extensive testing is still underway.
https://github.com/britta-wstnr/source_recon_langdysfun
For an introduction to source reconstruction, check out this lecture that I gave at Practical MEEG 2022 in Aix-en-Provence:
https://www.youtube.com/watch?v=kCNpFraJPEY
If you need an in-depth overview of beamforming, you can check out this paper we wrote:
Westner, BU, SS Dalal, A Gramfort, V Litvak, JC Mosher, R Oostenveld, and JM Schoffelen. 2022.
A Unified View on Beamformers for M/EEG Source Reconstruction.
NeuroImage 246.
https://doi.org/10.1016/j.neuroimage.2021.118789.
Data analysis of M/EEG data
The tutorials for M/EEG data analysis with MNE-Python from Practical MEEG 2022 are available online. The scripts
span preprocessing, ERP/ERF analysis, time-frequency analysis, source reconstruction, and MEG decoding.
Authors: Britta Westner, Alexandre Gramfort, and Denis Engemann
Find them here: https://zenodo.org/record/7602381
Teaching workshops
A blog post on some tips and tricks for teaching data analysis workshops.
Git and GitHub cheat sheet
For a presentation in the lab, I made a cheat sheet for git and GitHub workflows. You can view and download it here.