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Interpreting neural decoding models using grouped model reliance

This repository provides code and preprocessed data for the analyses presented in Valentin, Harkotte and Popov (2020).

Structure

  • EEG preprocessing and matlab-based analyses are provided in EEG_scripts/.
  • The implementation of grouped model reliance is provided in functions/model_reliance.py.
  • Feature groups, provided as indices of columns of the data matrices, can be found in feature_groups/.
  • The script compute_mr.py computes model reliance scores for both frequency and regions of interest and saves them to the results/ directory. For faster computation, consider lowering the number of trees for the Random Forest algorithm and/or the number of cross validation folds and random permutations. Running python compute_mr.py saves the results in .csv files to results/.
  • The main figures are generated in Fig2.ipynb and Fig3A.ipynb.
  • Analyses for supplementary figures are provided in separate notebooks as SX.ipynb.

Data

Preprocessed data matrices for each participant (with anonymised IDs) can be found under data/. Rows correspond to trials, and columns to power per frequency (in 1 Hz bins) per electrode location.