EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

🤖 Plain-English Summary

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. The experimental results show that our method achieves advanced performance and has great potential to be a new baseline for general EEG decoding.

🔑 Key Findings

• 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.

🔗 Related Resources