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

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Original authors have uploaded their code here https://github.com/vlawhern/arl-eegmodels

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.
  • I found that ELU/Linear/ReLU are similar in performance.

Results

  • BCI Competition 3 Dataset 2 - Fmeasure (0.402)

Credits

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

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