# EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

“EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization” (2022), by Yonghao Song et al. Neuroscience research paper. Original abstract below, unchanged. No free PDF was found.

- Authors: Yonghao Song, Qingqing Zheng, Bingchuan Liu, Xiaorong Gao
- Published: December 16, 2022
- Field: Neuroscience
- DOI: https://doi.org/10.1109/tnsre.2022.3230250
- Source: https://doi.org/10.1109/tnsre.2022.3230250

## Abstract

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 categories for EEG signals. To enhance interpretability, we also devise a visualization strategy to project the class activation mapping onto the brain topography. Finally, we have conducted extensive experiments to evaluate our method on three public datasets in EEG-based motor imagery and emotion recognition paradigms. The experimental results show that our method achieves state-of-the-art performance and has great potential to be a new baseline for general EEG decoding. The code has been released in https://github.com/eeyhsong/EEG-Conformer.

Canonical page: https://nerdi.pages.dev/paper/W4312597583
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