BErt-like Neurophysiological Data Representation

This repository contains the source code for reproducing, or extending the BERT-like self-supervision pre-training for EEG data from the article:

BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data

To run these scripts, you will need to use the DN3 project. We will try to keep this updated so that it works with the latest DN3 release. If you are just looking for the BENDR model, and don't need to reproduce the article results per se, BENDR will be (or maybe already is if I forgot to update it here) integrated into DN3, in which case I would start there.

Currently, we recommend version 0.2. Feel free to open an issue if you are having any trouble.

More extensive instructions are upcoming, but in essence you will need to either:

a)  Download the TUEG dataset and pre-train new encoder and contextualizer weights, _or_
b)  Use the [pre-trained model weights](https://github.com/SPOClab-ca/BENDR/releases/tag/v0.1-alpha)

Once you have a pre-trained model:

1) Add the paths of the pre-trained weights to configs/downstream.yml
2) Edit paths to local copies of your datasets in configs/downstream_datasets.yml
3) Run downstream.sh