Delineating neural contributions to electroencephalogram-based speech decoding

Public repository for Gmail interface papers using uhd-EEG
Author: Motoshige Sato¹, Yasuo Kabe¹, Sensho Nobe¹, Akito Yoshida¹, Masakazu Inoue¹, Mayumi Shimizu¹, Kenichi Tomeoka¹, Shuntaro Sasai^1*
1Araya Inc.

preparation

1. Install requirements package.

   uv sync

usage

1. Save preprocesed EEG/EMG.

   uv run python plot_figures/make_preproc_files.py

2. Visualization of preprocessing pipeline (Fig. 1)

   uv run python plot_figures/plot_preprocesssing.py

3. Visualization of volume of speech (Fig. 1) and RMS of EMGs (Fig. 2)

   uv run python plot_figures/plot_rms.py

4. Quantify the contamination level of EMG to EEG (mutual information, Fig. 2)

   uv run python plot_figures/plot_mis.py

5. Train decoders. You can specify in parallel_sets which subjects and which sessions' data to train.

   uv run python uhd_eeg/trainers/trainer.py -m hydra/launcher=joblib parallel_sets=subject1-1,subject1-2,subject1-3

6. Copy the trained models and metrics to data/
7. Run the inference for online data and evaluate metrics (Table 1, 2, Fig. S1)

   uv run python plot_figures/evaluate_accs.py

8. Visualization of electrodes used when hypothetically reducing electrode density (Fig. S1)

   uv run python plot_figures/show_montage_decimation.py

9. Analysis on decoding contributions (integrated gradients, Fig.3-5, Fig.S2)

   uv run python plot_figures/plot_contribution.py