Authors: Navid Mohammadi Foumani,
Geoffrey Mackellar, Soheila Ghane, Saad Irtza, Nam Nguyen, Mahsa Salehi
This work follows from the project with Emotiv Research, a bioinformatics research company based in Australia, and Emotiv, a global technology company specializing in the development and manufacturing of wearable EEG products.
EEG2Rep Paper: PDF
This is a PyTorch implementation of EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
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Emotiv: To download the Emotiv public datasets, please follow the link below to access the preprocessed datasets, which are split subject-wise into train and test sets. After downloading, copy the datasets to your Dataset directory.
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Temple University Datasets: Please use the following link to download and preprocess the TUEV and TUAB datasets.
Instructions refer to Unix-based systems (e.g. Linux, MacOS).
This code has been tested with Python 3.7 and 3.8.
pip install -r requirements.txt
To see all command options with explanations, run: python main.py --help
In main.py you can select the datasets and modify the model parameters.
For example:
self.parser.add_argument('--epochs', type=int, default=100, help='Number of training epochs')
or you can set the parameters:
python main.py --epochs 100 --data_dir Dataset/Crowdsource
If you find EEG2Rep useful for your research, please consider citing this paper using the following information:
```
@inproceedings{eeg2rep2024,
title={Eeg2rep: enhancing self-supervised EEG representation through informative masked inputs},
author={Mohammadi Foumani, Navid and Mackellar, Geoffrey and Ghane, Soheila and Irtza, Saad and Nguyen, Nam and Salehi, Mahsa},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={5544--5555},
year={2024}
}
```