Introduction

Author:	James R. McIntosh
Contact:	[email protected]

Purpose

• Code base to test machine learning approaches for EEG rhythm phase prediction.

• Related to the following publication:

  McIntosh JR, Sajda P. Estimation of phase in EEG rhythms for real-time applications. Journal of Neural Engineering. 2020 Jun 2;17(3):034002. doi: 10.1088/1741-2552/ab8683, PMID: 32244233.

• Read the full paper here: https://doi.org/10.1088/1741-2552/ab8683. Prior versions available on arXiv: https://arxiv.org/abs/1910.08784.

Installation and getting started

• After cloning the repository, the following commands will install the necessary libraries:

conda env create -f py_eegepe.yml

• To get started, an across subject analysis as described in the manuscript can be run.

conda activate py_eegepe
cd examples/
python gen_figure_supp_as.py

• Before the gen_figure_supp_as.py python scrip will work, the data has to be correctly configured in two locations:

  • gen_figure_supp_as.py : datadir variable (the root of the dataset folder), dataset variable (the name of the dataset folder)
  • data_specific.py : specifier, specifier_proc and _sublist which act to configure the location of data within the project directory. As well as preprocessor which handles the data specific pre-processing to be carried out (see examples for existing datasets within data_specific.py)

• Examples directory has been configured to operate with the Child Mind Institute - healthy brain network data. After downloading this data must be processed by a matlab script (convert_mat_to_eeglab.m) to enable loading with MNE.

  • The path structure should look like this:

  • [datadir]/data/[subject_ID]/EEG/raw/mat_format/

  • And where resting data is present, the following will be added after running the matlab script:

  • [datadir]/data/[subject_ID]/EEG/raw/eeglab_format/

  • data_specific.py is currently setup to deal with this configuration, but this can be changed as is required.

• gen_figure_supp_as.py as well as the other high-level functions in examples/ and manuscripts/ are configured to cycle through a series of experiments as configured in paradigm.py

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