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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
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
Items 101–129 of 129
Attention Is All Your Brain Needs: How Transformer Models Advance Neurotechnology—A Mini Review
Alzahab, N. A. (2026) · AI in Neurology · doi:10.64187/ain.2026.v1.i3.002
Xu, M., Lin, C., Liu, C., Zhang, X., Zhou, X., Gong, P., Jia, Z., Pan, Y., & Ding, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7149764
Self-citationVariational spatiotemporal enhanced transformer encoder-decoder for robust EEG signal recognition
Wang, P. P., Gao, M., Gao, H., & Li, J. J. (2026) · Connection Science · doi:10.1080/09540091.2026.2705021
Emotion Recognition Model using a Graph-Enhanced Spatiotemporal EEG-Deformer
Kim, H.-G., & Kim, M.-S. (2026) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2026.24.7.231
Multi-Scale BiMamba with Test-Time Adaptation for Motor Imagery Classification
Bao, Z., Guo, M., Jin, B., Zhao, Y., Yang, B., Wu, Y., Wang, K., & Liu, C. (2026) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3833427
Self-citationA Comprehensive Benchmark of EEG-Based BCI Deep Learning Models for MCI and Dementia ClassificationPreprint
Zaitsev, V., & Wei, C.-S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.12.743255
Wei, C., Rao, X., Hu, B., Zeng, J., Yi, P., Shao, H., Tan, Q., Xie, Y., Fang, L., Gong, Y., et al. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/aea18f
Liu, K., Zhu, W., Xu, R., Wang, W., Yu, Z., & Wu, W. (2026) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0701
Chen, H., Zhou, Z., Chen, D., Wang, Y., Gao, T., Liu, X., & Chen, J. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae9ef0
Performance Evaluation of Hybrid CNN-Transformer Architectures for EEG-Based Data
Savadkar, A., Borole, Y., & Kadam, A. (2026) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2026.v08i05.87030
Ahmadi, H., & Mesin, L. (2026) · Sensors · doi:10.3390/s26185728
Hybrid CNN-Transformer with PSD Features for EEG-Based Driver Drowsiness Detection
Mohammed, A., Alneqrat, F. M., Alfughi, H. G., & Aghnaya, H. A. (2026) · Journal of Electrical and Electronic Engineering and Information Technology · doi:10.68226/0e8d6e76
Ma, W., Zhang, H., Chen, S., Chen, G. T. Y., Zhang, S., Yu, Z., & Wei, M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111444
Du, K., Chang, W., Kong, W., Huang, H., Yan, G., & Sadiq, M. T. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/aeabf5
CSDD-Net: A Framework for Cross-Subject Depression Detection from EEG Signals
Zhang, L., Xu, C.-Y., Li, W.-Q., Hou, H.-R., & Meng, Q.-H. (2026) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc66907.2026.11694817
Liu, P., Yang, B., Guo, W., Chen, J., & Kuang, S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111567
Effects of EEG-data normalization on EEG-Transformer-based motor classification
Mattei, E., & Lozzi, D. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21929-9
Zhang, K., Pan, H., Zenan, L., Chen, Y., Yan, S., Jia, D., Chen, F., Li, J., Tong, M. C. F., Zhu, M., et al. (2026) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2026.3743175
Mewada, D., Roy, S., & Sarma, M. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3858339
Li, S., Daly, I. P., Qin, K., Wang, X., Cichocki, A. S., & Jin, J. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109768
Dual-Branch Temporal-Manifold EEG Feature Fusion for Audio-Induced Emotion Recognition
Wang, L., Hu, S., Zhong, W., Geng, B., Liu, Y., & Ye, L. (2026) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew69720.2026.11720840
SMEEG: Spectral Mamba-Driven EEG Temporal and Frequency Representation Learning
Lin, B., Lin, H., Zheng, B. R., Zhu, S., & 吴茂念 (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729686
Myat, A. K., Pillai, B. M., Polprasert, C., & Ekpanyapong, M. (2026) · International Conference on Advanced Robotics and Intelligent Systems (ARIS) · doi:10.1109/aris70457.2026.11734468
Transformer networks are robust learners of source localization for EEG measurements
Hart, D. M., Polichtchouk, S., & Dannhauer, M. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/aeb82c
Wen, Y., & Shi, C. (2026) · International Conference on Big Data and Intelligent Medicine (BDIMed) · doi:10.1145/3816325.3843667
Choi, W.-S., & Yeom, H. G. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1973838
Gao, S., & Lv, L. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1997832
DTEF-Net: Dual-Path Temporal-Evidence Fusion for Motor-Imagery EEG Decoding
何开森, Jin, H., & Wang, Z. (2026) · doi:10.1109/aice71085.2026.11736951
Qi, Y., Ling, H., Li, Z., & Yang, X. (2026) · doi:10.1145/3840470.3841778
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
