Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Open accessAbstract
A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications—motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks—are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.
Integrity signals
Registry
Cites
Statistics
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?
Domain adaptation and domain generalization methods address cross-subject distribution shift in ERP-based BCIs but are not designed for interpretability.
“Domain adaptation and domain generalization methods address cross-subject distribution shift in ERP-based BCIs [20], [21], [22], but are not designed for interpretability.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [21]
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 1× · see citing works
“, happy, sad, and angry) and by continuous values in the 2D space of arousal and valence [80] or the 3D space of arousal, valence, and dominance [81].”
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 8× · see citing works
“Experiments on cross-subject visual evoked potential classification demonstrated that SAN outperformed a support vector machine (SVM) and EEGNet.”
InfluentialDeep 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 · see citing works
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239 · see citing works
Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He, H., & Wu, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913914 · Cited by 525 · cited 2× · see citing works
“For example, EA and LA data alignment schemes have been combined with TL classifiers [50], [60], and CSDF-ReliefF and CSDF-mRMR feature selection approaches have also been integrated with TL classifiers [114].”
Self-citationA multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424 · cited 2× · see citing works
“There are many important BCI regression problems, e.g., driver drowsiness estimation [38]–[40], vigilance estimation [11], [12], [113], and user reaction time estimation [41], which were not adequately addressed in previous reviews.”
Self-citationTransfer Learning: A Riemannian Geometry framework with applications to Brain-Computer Interfaces
Zanini, P., Congedo, M., Jutten, C., Said, S., & Berthoumieu, Y. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2742541 · Cited by 412 · see citing works
Riemannian geometry applied to BCI classification
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15995-4_78 · Cited by 233 · see citing works
Brain–Computer Interface Technologies in the Coming Decades
Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S., & McDowell, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184830 · Cited by 231 · see citing works
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.323 · Cited by 154 · cited 1× · see citing works
“Currently, a common TL technique for deep learning-based EEG classification [68], [69] is based on finetuning with new data from the target session/subject.”
MethodA review of rapid serial visual presentation-based brain–computer interfaces
Lees, S., Dayan, N., Cecotti, H., McCullagh, P., Maguire, L., Lotte, F., & Coyle, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9817 · Cited by 149 · see citing works
Online and Offline Domain Adaptation for Reducing BCI Calibration Effort
Wu, D. (2017) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2608931 · Cited by 114 · see citing works
Self-citationWaytowich, N. R., Lawhern, V. J., Bohannon, A. W., Ball, K. R., & Lance, B. J. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00430 · Cited by 79 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Guo, S., Wang, Y., Liu, Y., Zhang, X., & Tang, B. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107756 · Cited by 7 · see citing works
ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification
Genevier Wyman, C., & Hirshfield, L. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae9f95
- Authors
- Wu, D., Xu, Y., & Lu, B.-L.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Venue
- IEEE Transactions on Cognitive and Developmental Systems
- Published
- 2022-03
- Volume
- 14
- Issue
- 1
- Pages
- 4-19
- ISSN
- 2379-8920 (ISSN-L)
- 2379-8939
- OpenAlex
- W3041698047
- Cited by
- 434
- References
- 15
- Citations by year
- 2026: 81 · 2025: 100 · 2024: 97 · 2023: 69 · 2022: 49 · 2021: 28 · 2020: 10
- FWCI
- 20.96
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Cognitive and Developmental Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Science Fund for Distinguished Young Scholars of Hubei Province (2020CFA050)
- Hubei Technology Innovation Platform (2019AEA171)
- National Natural Science Foundation of China (61873321, U1913207, 61673266, 61976135)
- International Science and Technology Cooperation Program of China (2017YFE0128300)
- National Key Research and Development Program of China (2017YFB1002501)
- SJTU Transmed Awards Research (WF540162605)
- Fundamental Research Funds for the Central Universities
- Higher Education Discipline Innovation Project
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- transfer learning, EEG-based brain-computer interface, non-stationarity, cross-subject transfer, cross-device transfer, cross-task transfer, motor imagery, event-related potentials, steady-state visual evoked potentials, affective brain-computer interfaces, regression analysis, adversarial attacks, calibration reduction, pattern recognition
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
