EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
<|reference_start|>EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces: Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasks consistently verify the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms existing state-of-the-art methods or is comparable to them. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks. The source code can be found at https://github.com/yi-ding-cs/EEG-Deformer.<|reference_end|>
@article{ding2024eeg-deformer:,
    title={EEG-Deformer: A Dense Convolutional Transformer for Brain-computer
  Interfaces},
    author={Yi Ding, Yong Li, Hao Sun, Rui Liu, Chengxuan Tong, Chenyu Liu,
  Xinliang Zhou, Cuntai Guan},
    journal={arXiv preprint arXiv:2405.00719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00719},
    primaryClass={eess.SP cs.LG q-bio.NC}
}