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
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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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · cited 5×
“CNNs along temporal and spatial dimensions are commonly used as feature extractors for EEG signals [13].”
MethodInfluentialDeep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Nunez, P. L., & Srinivasan, R. (2006) · doi:10.1093/acprof:oso/9780195050387.001.0001 · Cited by 2,531
MNE software for processing MEG and EEG data
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C. J., Parkkonen, L. T., & Hämäläinen, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.027 · Cited by 2,035
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Song, T., Zheng, W., Song, P., & Cui, Z. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2817622 · Cited by 1,646
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
Song, Y., Zheng, Q., Liu, B., & Gao, X. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3230250 · Cited by 1,139
Research works printed but not linked: 2
- Dosovitskiy, 2021, An image is worth 16x16 words: Transformers for image recognition at scale
- 10.1109/BCI53720.2022.9735124 · DOI
Cited by
Sorted by citations
Vafaei, E., & Hosseini, M. (2025) · Sensors · doi:10.3390/s25051293 · Cited by 147
Cheng, C., Liu, W., Feng, L., & Jia, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106624 · Cited by 39
Temporal convolutional transformer for EEG based motor imagery decoding
Altaheri, H., Karray, F., & Karimi, A.-H. (2025) · Scientific Reports · doi:10.1038/s41598-025-16219-7 · Cited by 41
Ghous, G., Najam, S., Alshehri, M., Alshahrani, A., AIQahtani, Y., Jalal, A., & Liu, H. (2025) · IEEE Access · doi:10.1109/access.2025.3561137 · Cited by 35
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Wang, Z., Wang, H., Jia, T., He, X., Li, S., & Wu, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3622725 · Cited by 18
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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