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Code for data analysis of NeurIPS19 paper "Manifold-regression to predict from MEG/EEG brain signals without source modeling"
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README.rst
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README.rst

Regression with covariance matrices

This is the Python code for the NeurIPS 2019 article Manifold-regression to predict from MEG/EEG brain signals without source modeling

Dependencies

Libraries

  • 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

Main scripts

  • 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)
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