This is the Python code for the NeurIPS 2019 article Manifold-regression to predict from MEG/EEG brain signals without source modeling
- numpy >= 1.15
- scipy >= 1.12
- matplotlib >= 3.0
- scikit-learn >= 0.20
- pyriemann (https://github.com/alexandrebarachant/pyRiemann)
- library/preprocessing.py contains the code used to preprocess raw data from CamCAN
- /library/spfiltering.py contains the functions to implement spatial filtering of the covariance matrices
- /library/featuring.py contains all the functions to vectorize the covariance matrices
- library/simuls: contains the function to generate covariance matrices following the generative model of the paper
- /library/utils.py contains the other vectorization methods
- nips_simuls_compute_ are the 3 scripts used for the 3 simulations of the paper
- nips_simuls_plot_ are the corresponding plotting scripts (in R)