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Yonghao Song, Qingqing Zheng, Bingchuan Liu, Xiaorong Gao
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.
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Song, Y., Zheng, Q., Liu, B., & Gao, X. (2022). EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 710–719. https://doi.org/10.1109/tnsre.2022.3230250
Song, Yonghao, et al. "EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization." IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, 2022, pp. 710–719, https://doi.org/10.1109/tnsre.2022.3230250.
Song, Yonghao, Qingqing Zheng, Bingchuan Liu, and Xiaorong Gao. "EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization." IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2022): 710–719. https://doi.org/10.1109/tnsre.2022.3230250.
Song Y., Zheng Q., Liu B. [и др.] EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization // IEEE Transactions on Neural Systems and Rehabilitation Engineering. — 2022. — Vol. 31. — P. 710–719. — URL: https://doi.org/10.1109/tnsre.2022.3230250
@article{song2022conformer,
title = {{EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization}},
author = {Yonghao Song and Qingqing Zheng and Bingchuan Liu and Xiaorong Gao},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
year = {2022},
volume = {31},
pages = {710--719},
doi = {10.1109/tnsre.2022.3230250},
url = {https://doi.org/10.1109/tnsre.2022.3230250}
}TY - JOUR AU - Song, Yonghao AU - Zheng, Qingqing AU - Liu, Bingchuan AU - Gao, Xiaorong TI - EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering VL - 31 SP - 710 EP - 719 PY - 2022 DA - 2022/12/16 DO - 10.1109/tnsre.2022.3230250 UR - https://doi.org/10.1109/tnsre.2022.3230250 ER -
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