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Thanks toBru and colleagues for helping with the modifications.

• We propose a compact convolutional Transformer, 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.

• Python 3.10
• Pytorch 1.12

Please use consistent train-val-test split when comparing with other methods.

• BCI_competition_IV2a - acc 78.66% (hold out)
• BCI_competition_IV2b - acc 84.63% (hold out)
• SEED - acc 95.30% (5-fold)

Hope this code can be useful. I would appreciate you citing us in your paper. 😊

@article{song2023eeg,  title = {{{EEG Conformer}}: {{Convolutional Transformer}} for {{EEG Decoding}} and {{Visualization}}},  shorttitle = {{{EEG Conformer}}},  author = {Song, Yonghao and Zheng, Qingqing and Liu, Bingchuan and Gao, Xiaorong},  year = {2023},  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},  volume = {31},  pages = {710--719},  issn = {1558-0210},  doi = {10.1109/TNSRE.2022.3230250}}