EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces

• URL: http://arxiv.org/abs/2405.00719v2
• Date: Tue, 29 Oct 2024 06:24:00 GMT
• Title: EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
• Authors: Yi Ding, Yong Li, Hao Sun, Rui Liu, Chengxuan Tong, Chenyu Liu, Xinliang Zhou, Cuntai Guan,
• Abstract summary: We introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer. EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks.
• Score: 17.524441950422627
• License:
• Abstract: 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-cognitive attention, driving fatigue, and mental workload detection-consistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. 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.

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