EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classification framework. Specifically, 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…

• NN
  National Natural Science Foundation of China

  Awards: 62206270, 62171473, U2241208
• TU
  Tsinghua University
• KR
  Key Research and Development Program of Ningxia

  Award: 2022CMG02026
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2021A1515110598