nbara/EEGNet

PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf

Jupyter NotebookMIT

EEGNet

PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

Requirements

Python 2
Dataset of your own choice, works well with BCI Competition 3 Dataset 2.
Pytorch 0.2+
Jupyter notebook

Usage

GPU - Just shift+enter everything.
No GPU - Remove all .cuda(0) before running.

Notes

I found ELU to work inferior, would not recommend. Linear units work better than ReLU as well.

Results

BCI Competition 3 Dataset 2 - Fmeasure (0.402)

Credits

Original paper - https://arxiv.org/abs/1611.08024
PyTorch documentation.

Hope this helped you. Raise an issue if you spot errors or contact sriram@ucsd.edu.