ERP classification using spatiotemporal beamforming as implemented in [1]. This software makes use of the MNE-Python toolbox [2], the BIDS-EEG format [3], MNE-BIDS [4] and MNE-BIDS-Pipeline. A comparative classifier [5] is implemented with pyRiemann
git clone https://github.com/kul-compneuro/stbf-erp.git --recurse-submodules
cd stbf-erp
virtualenv -p python3 .venv
source .venv/bin/activate
pip install -r preprocessing/mne_bids_pipeline/requirements.txt
pip install -r requirements.txtMake sure the MNE_DATA directory is correctly configured
(https://mne.tools/stable/auto_tutorials/intro/50_configure_mne.html?highlight=configuration).
Download ERP_CORE_BIDS_Raw_Files from https://osf.io/thsqg/ and extract it to $MNE_DATA/ERP_CORE_BIDS_Raw_Files
Preprocess the ERP-CORE P3 dataset by executing
preprocessing/mne_bids_pipeline/run.py --config preprocessing/erp_core_P3.py[1] Van Den Kerchove, A.; Libert, A.; Wittevrongel, B.; Van Hulle, M.M. Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming. (in review)
[2] Gramfort, A.; Luessi, M.; Larson, E.; Engemann, D.A.; Strohmeier, D.; Brodbeck, C.; Goj, R.; Jas, M.; Brooks, T.; Parkkonen, L.; 553 et al. MEG and EEG Data Analysis with MNE-Python. Frontiers in Neuroscience 2013, 7, 1–13. doi:10.3389/fnins.2013.00267.
[3] Pernet, C.R.; Appelhoff, S.; Gorgolewski, K.J.; Flandin, G.; Phillips, C.; Delorme, A.; Oostenveld, R. EEG-BIDS, an extension to the 555 brain imaging data structure for electroencephalography. Scientific Data 2019, 6, 103. doi:10.1038/s41597-019-0104-8.
[4] Appelhoff, S.; Sanderson, M.; Brooks, T.L.; Vliet, M.v.; Quentin, R.; Holdgraf, C.; Chaumon, M.; Mikulan, E.; Tavabi, K.; 557 Höchenberger, R.; et al. MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. 558 Journal of Open Source Software 2019, 4, 1896. doi:10.21105/joss.01896.
[5] Barachant, A. MEG decoding using Riemannian Geometry and Unsupervised classification. Grenoble University: Grenoble, France 560 2014.