EEG-Conformer

EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization [Paper]

Core idea: spatial-temporal conv + pooling + self-attention

Abstract

• We propose a compact convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classification framework.
• The convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is straightforwardly connected to extract the global correlation within the local temporal features. Subsequently, the simple classifier module based on fully-connected layers is followed to predict the categories for EEG signals.
• We also devise a visualization strategy to project the class activation mapping onto the brain topography.

Requirmenets:

• Python 3.10
• Pytorch 1.12

Datasets

• BCI_competition_IV2a
• BCI_competition_IV2b
• SEED

Citation

Hope this code can be useful. I would be very appreciate if you cite us in your paper. 😄

@article{song2021eegconformer,
  author={Song, Yonghao and Zheng, Qingqing and Liu, Bingchuan and Gao, Xiaorong},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, 
  title={EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization}, 
  year={2023},
  volume={},
  pages={},
  doi={}
}