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EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces
Open accessAbstract
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 tasksâcognitive attention, driving fatigue, and mental workload detectionâconsistently 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.
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“dense convolutional transformers such as EEG-Deformer [11] have improved decoding across a range of EEG tasks by capturing coarse- and fine-grained temporal dynamics within each transformer block.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [11]
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Metadata
- Authors
- Ding, Y., Li, Y., Sun, H., Liu, R., Tong, C., Liu, C., Zhou, X., & Guan, C.
- Affiliations
- Nanyang Technological University (SG)
- East China University of Science and Technology (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2025-03
- Volume
- 29
- Issue
- 3
- Pages
- 1909-1918
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4404576998
- Cited by
- 120
- References
- 41
- Citations by year
- 2026: 63 · 2025: 53
- FWCI
- 29.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- RIE2020 AME Programmatic Fund, Singapore (A20G8b0102)
- MoE AcRF Tier 1 Project (RT01/21)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- EEG signal processing, brain-computer interface, EEG decoding, CNN-Transformer, temporal dynamics, attention, driver fatigue detection, EEG source localization
- MeSH terms
- Adult, Algorithms, Brain, Electroencephalography, Humans, Male, Signal Processing, Computer-Assisted, Neural Networks, Computer, Young Adult, Brain-Computer Interfaces
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