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Interpreting linear Riemannian tangent space (RTS) models

This repository contains code for a EMBC 2021 submitted article On the interpretation of linear Riemannian tangent space model parameters in M/EEG

The repository was forked on the repository: https://github.com/DavidSabbagh/meeg_power_regression

Python dependencies

  • numpy >= 1.20
  • scipy >= 1.12
  • matplotlib >= 3.3
  • scikit-learn >= 0.23
  • pandas >= 1.2.1
  • mne >= 0.22
  • h5py >= 3.1
  • pyriemann >= 0.2

Repository structure

The root directory contains Python scripts that can be run to simulations (sim_*.py) and analyze a publicly available MEG dataset (dscmc_regression.py) that was recorded to study cortico-muscular coherence (CMC). The plots for the paper were generated using R. The scripts end with (_plots.R). Some paths and plotting options are defined in the configuration files (config.[py|r])

The library folder contains several utility functions and classes that are used in the analysis scripts.

References

Sabbagh, David, et al. "Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states." NeuroImage 222 (2020): 116893. https://doi.org/10.1016/j.neuroimage.2020.116893

Kobler, Reinmar et al. "On the interpretation of linear Riemannian tangent space model parameters in M/EEG." EMBC 2021 (accepted version). https://arxiv.org/abs/2107.14398