Original authors have uploaded their code here https://github.com/vlawhern/arl-eegmodels
PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
- Python 2
- Dataset of your own choice, works well with BCI Competition 3 Dataset 2.
- Pytorch 0.2+
- Jupyter notebook
- GPU -
Just
shift+enter
everything. - No GPU -
Remove all
.cuda(0)
before running.
I found ELU to work inferior, would not recommend. Linear units work better than ReLU as well.- I found that ELU/Linear/ReLU are similar in performance.
- BCI Competition 3 Dataset 2 - Fmeasure (0.402)
- Original paper - https://arxiv.org/abs/1611.08024
- PyTorch documentation.
Hope this helped you. Raise an issue if you spot errors or contact [email protected].