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

Ding, Yi; Li, Yong; Sun, Hao; Liu, Rui; Tong, Chengxuan; Liu, Chenyu; Zhou, Xinliang; Guan, Cuntai

doi:10.1109/jbhi.2024.3504604

Cited by 129EEG and Brain-Computer InterfacesEmotion and Mood RecognitionGaze Tracking and Assistive Technology

The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.

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

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Ding, Yi, Li, Yong, Sun, Hao, Liu, Rui, Tong, Chengxuan, Liu, Chenyu, Zhou, Xinliang, Guan, Cuntai·2025·IEEE Journal of Biomedical and Health Informatics

DOI 10.1109/jbhi.2024.3504604

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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 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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EEG-Deformer has improved decoding across a range of EEG tasks by capturing coarse- and fine-grained temporal dynamics within each transformer block.
“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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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)
ORCID: Ding, Yi
Li, Yong
Sun, Hao
Liu, Rui
Tong, Chengxuan
Liu, Chenyu
Zhou, Xinliang
Guan, Cuntai
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2025-03
Volume: 29
Issue: 3
Pages: 1909-1918
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2024.3504604
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
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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EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces

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