Neural decoding from stereotactic EEG: accounting for electrode variability across subjects

In this work, we introduce a training framework and architecture that can be used for multi-subject neural decoding based on stereotactic electroencephalography (sEEG). We use our framework to decode the trial-wise response time of subjects performing a behavioral task solely from their neural data. For more information, please refer to our project page.

To protect the privacy of the people that kindly shared their sEEG data with us (and to compy with HIPAA regulations), we cannot make our dataset public. To help you structure your data in a way that can be processed by our framework, we provide synthetic (fake) sEEG data for 3 "subjects". You can find the synthetic data here.

Installation:

In a clean virtual environment with python 3.8.10, run the following to clone the repository and download the synthetic data:

git clone https://github.com/gmentz/seegnificant.git
cd seegnificant
pip install -e .
gdown --folder https://drive.google.com/drive/folders/1UFSRT3wGNYZAXdpndDyRHr-CmjRQPlbM -O data

Getting started:

For an easy-to-follow introduction to our framework, please follow along the example.ipynb.

Alternatively, you can run individual components of the framework by running:

• python3 -m Signal_Processing.harmonize
• python3 -m Signal_Processing.FDR_correction
• python3 -m Model_and_Training.sEEGDataset
• python3 -m Model_and_Training.SingleSubjectTrain
• python3 -m Model_and_Training.MultiSubjectTrain
• python3 -m Model_and_Training.TransferPreTrained

Citation

We hope that you will find this code useful. If you do, please consider citing our work as:

@inproceedings{
    mentzelopoulos2024neural,
    title={Neural decoding from stereotactic EEG: accounting for electrode variability across subjects},
    author={Mentzelopoulos, Georgios and Chatzipantazis, Evangelos and Ramayya, Ashwin G and Hedlund, Michelle and Buch, Vivek and Daniilidis, Kostas and Kording, Konrad and Vitale, Flavia},
    booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
    }

Acknowledgments

We would like to thank the authors of the following repositories for making their code publicly available.

• EEG-Conformer
• POYO