Online and Offline Domain Adaptation for Reducing BCI Calibration Effort
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Many real-world brain–computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about$\text{50}\%$, making them more suitable for real-time applications.
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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 [20]
The Problem Definition (p. 3) describes domain adaptation between subjects whose EEG response distributions differ, supporting the cross-subject shift claim for ERP-based BCIs. Our copy does not establish the domain-generalization part. The interpretability contrast follows the citation and is not treated as a finding attributed to this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTransfer learning has been successfully used for BCIs.
“Transfer learning (TL) [23], which utilizes information in source domains to improve the learning performance in a target domain, has also been successfully used for BCIs [12], [35], [36], [38], [39].”
From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [36]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
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
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Multiple Comparisons Using Rank Sums
Dunn, O. J. (1964) · Technometrics · doi:10.1080/00401706.1964.10490181 · Cited by 5,168 · see citing works
Multiple comparisons among means
Dunn, O. J. (1961) · Journal of the American Statistical Association · doi:10.1080/01621459.1961.10482090 · Cited by 5,006 · see citing works
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791 · see citing works
Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works
(2006) · Cited by 3,624 · see citing works
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295 · see citing works
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785 · cited 1× · see citing works
“As a result, we used Friedman’s test [15], a two-way non-parametric ANOVA where column effects are tested for significant differences after adjusting for possible row ef fects.”
MethodA two-stage model for multiple target detection in rapid serial visual presentation.
Chun, M. M., & Potter, M. C. (1995) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.21.1.109 · Cited by 1,431 · see citing works
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
Lotte, F., & Guan, C. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2082539 · Cited by 1,047 · see citing works
Short-term conceptual memory for pictures.
Potter, M. C. (1976) · Journal of Experimental Psychology Human Learning & Memory · doi:10.1037/0278-7393.2.5.509 · Cited by 1,022 · see citing works
Zander, T. O., & Kothe, C. A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025005 · Cited by 906 · see citing works
Multiclass Brain–Computer Interface Classification by Riemannian Geometry
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172210 · Cited by 878 · see citing works
Adaptation Regularization: A General Framework for Transfer Learning
Long, M., Wang, J., Ding, G., Pan, S. J., & Yu, P. S. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.111 · Cited by 609 · see citing works
Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., & Grosse-Wentrup, M. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2501545 · Cited by 468 · see citing works
Class Imbalance Problem in Data Mining ReviewPreprint
Longadge, R., & Dongre, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.1707 · Cited by 437 · see citing works
Crottaz‐Herbette, S., & Menon, V. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2006.18.5.766 · Cited by 384 · see citing works
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327 · see citing works
Transferring Subspaces Between Subjects in Brain--Computer Interfacing
Samek, W., Meinecke, F. C., & Müller, K. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2253608 · Cited by 216 · see citing works
Beamforming in Noninvasive Brain–Computer Interfaces
Grosse‐Wentrup, M., Liefhold, C., Gramann, K., & BUSS, M. J. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2009768 · Cited by 182 · see citing works
Brain Activity-Based Image Classification From Rapid Serial Visual Presentation
Bigdely-Shamlo, N., Vankov, A., Ramirez, R. R., & Makeig, S. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2003381 · Cited by 177 · see citing works
Evolving Signal Processing for Brain–Computer Interfaces
Makeig, S., Kothe, C. A., Mullen, T. R., Bigdely-Shamlo, N., Zhang, Z., & Kreutz-Delgado, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2185009 · Cited by 163 · see citing works
What is odd in the oddball task?
Huettel, S. A., & McCarthy, G. (2003) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2003.07.009 · Cited by 162 · see citing works
In a Blink of an Eye and a Switch of a Transistor: Cortically Coupled Computer Vision
Sajda, P., Pohlmeyer, E. A., Wang, J., Parra, L. C., Christoforou, C., Dmochowski, J., Hanna, B. P., Bahlmann, C., Singh, M. K., & Chang, S.-F. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2038406 · Cited by 151 · see citing works
Learning from other subjects helps reducing Brain-Computer Interface calibration time
Lotte, F., & Guan, C. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495183 · Cited by 148 · see citing works
Spatiotemporal Linear Decoding of Brain State
Parra, L. C., Christoforou, C., Gerson, A. D., Dyrholm, M., Luo, A., Wagner, M., Philiastides, M. G., & Sajda, P. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408447 · Cited by 135 · see citing works
A Comparison of Electroencephalography Signals Acquired from Conventional and Mobile Systems
Ries, A. J., Touryan, J., Vettel, J. M., McDowell, K., & Hairston, W. D. (2014) · Journal of Neuroscience and Neuroengineering · doi:10.1166/jnsne.2014.1092 · Cited by 104 · see citing works
Multitask Learning for Brain-Computer Interfaces
Alamgir, M., Grosse‐Wentrup, M., & Altün, Y. (2010) · Cited by 89 · see citing works
Pohlmeyer, E. A., Wang, J., Jangraw, D. C., Lou, B., Chang, S., & Sajda, P. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/036025 · Cited by 85 · see citing works
Wu, D., Lawhern, V. J., Hairston, W. D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2544108 · Cited by 74 · see citing works
Self-citationLi, Y., & Guan, C. (2007) · Machine Learning · doi:10.1007/s10994-007-5039-1 · Cited by 70 · see citing works
Wu, D., Lance, B. J., & Parsons, T. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056624 · Cited by 70 · see citing works
Self-citationAmplitude differences of evoked alpha and gamma oscillations in two different age groups
Böttger, D., Herrmann, C. S., & von Cramon, D. Y. (2002) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(02)00031-4 · Cited by 67 · see citing works
A review on transfer learning for brain-computer interface classification
Wang, P., Lu, J., Zhang, B., & Tang, Z. (2015) · International Conference on Information Science and Technology · doi:10.1109/icist.2015.7288989 · Cited by 66 · see citing works
Lomasky, R., Brodley, C. E., Aernecke, M. J., Walt, D. R., & Friedl, M. A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_63 · Cited by 64 · see citing works
Marathe, A. R., Lawhern, V. J., Wu, D., Slayback, D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2502323 · Cited by 64 · see citing works
Self-citationAging effect in pattern, motion and cognitive visual evoked potentials
Kuba, M., Kremláček, J., Langrová, J., Kubová, Z., Szanyi, J., & Vı́t, F. (2012) · Vision Research · doi:10.1016/j.visres.2012.03.014 · Cited by 56 · see citing works
Wu, D., Lance, B. J., & Lawhern, V. J. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974353 · Cited by 50 · see citing works
Self-citationOnline driver's drowsiness estimation using domain adaptation with model fusion
Wu, D., Chuang, C., & Lin, C. (2015) · International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344682 · Cited by 34 · see citing works
Self-citationWu, D., Lawhern, V. J., Gordon, S. M., Lance, B. J., & Lin, C. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844328 · Cited by 34 · see citing works
Self-citationWu, D., Lawhern, V. J., & Lance, B. J. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.557 · Cited by 25 · see citing works
Self-citationEfficient Labeling of EEG Signal Artifacts Using Active Learning
Lawhern, V. J., Slayback, D., Wu, D., & Lance, B. J. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.558 · Cited by 21 · see citing works
Self-citationWu, D., Lawhern, V. J., & Lance, B. J. (2015) · International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344626 · Cited by 16 · see citing works
Self-citationVisual evoked potentials: Phenotypic and genotypic variability
Bulayeva, K., Pavlova, T. A., & Guseynov, G. G. (1993) · Behavior Genetics · doi:10.1007/bf01067978 · Cited by 9 · see citing works
Flagged first, then by citations
2 of the 51 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- [2][2] A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, 'Multiclass braincomputer interface classification by Riemannian geometry,' IEEE Trans. on Biomedical Engineering , vol. 59, no. 4, pp. 920-928, 2012. -
- [19][19] V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, and M. GrosseWentrup, 'Transfer learning in brain-computer interfaces,' IEEE Computational Intelligence Magazine , vol. 11, no. 1, pp. 20-31, 2016. -
- [20][20] P.-J. Kindermans, H. Verschore, D. Verstraeten, and B. Schrauwen, 'A P300 BCI for the masses: Prior information enables instant unsupervised spelling,' in Proc. Neural Information Processing Systems (NIPS) , Lake Tahoe, NV, December 2012. -
- [37][37] F. Provost, 'Machine learning from imbalanced data sets 101,' Tech. Rep. AAAI Technical Report WS-00-05, 2000. -
- [45][45] V. Vapnik, Statistical Learning Theory . New York, NY: Wiley Press, 1998. -
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Bian, R., Meng, L., & Wu, D. (2022) · Science China Information Sciences · doi:10.1007/s11432-022-3440-5 · Cited by 22 · see citing works
Self-citationHe, H., & Wu, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70096-0_83 · Cited by 22 · see citing works
Self-citationJafari, M., Tao, X., Barua, P. D., Tan, R., & Acharya, U. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102982 · Cited by 22 · see citing works
Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD
Yao, L., Jiang, N., Mrachacz‐Kersting, N., Zhu, X., Farina, D., & Wang, Y. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3184402 · Cited by 21 · see citing works
What External Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?
Horowitz, A. J., Guger, C., & Korostenskaja, M. (2021) · HCA Healthcare Journal of Medicine · doi:10.36518/2689-0216.1188 · Cited by 21 · see citing works
Revisiting Euclidean alignment for transfer learning in EEG-based brain–computer interfaces
Wu, D. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/addd49 · Cited by 19 · see citing works
Self-citationUltra Efficient Transfer Learning with Meta Update for Cross Subject EEG ClassificationPreprint
Duan, T., Chauhan, M., Shaikh, M. A., Chu, J., & Srihari, S. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06113 · Cited by 19 · see citing works
Feature-Selection-Based Transfer Learning for Intracortical Brain–Machine Interface Decoding
Zhang, P., Li, W., Ma, X., He, J., Huang, J., & Li, Q. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3034234 · Cited by 17 · see citing works
Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning
Mao, W., Wei, B., Xu, X., Chen, L., Wu, T., Peng, Z., & Ren, C. (2022) · Sensors · doi:10.3390/s22124470 · Cited by 17 · see citing works
Active semi-supervised transfer learning (ASTL) for offline BCI calibration
Wu, D. (2017) · 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2017.8122610 · Cited by 16 · see citing works
Self-citationOvercoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions
Rostami, M. G., & Galstyan, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26151 · Cited by 16 · see citing works
Domain Adaptation in Children Activity Recognition
Hosseini, A., Zamanzadeh, D. J., Valencia, L., Habre, R., Bui, A. A. T., & Sarrafzadeh, M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857135 · Cited by 15 · see citing works
Cao, L., Chen, S., Jia, J., Fan, C., Wang, H., & Xu, Z. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.629572 · Cited by 15 · see citing works
Multi-source domain adaptation for decoder calibration of intracortical brain-machine interface
Li, W., Ji, S., Chen, X., Kuai, B., He, J., Zhang, P., & Li, Q. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc528 · Cited by 14 · see citing works
Ghosh, L., Konar, A., Rakshit, P., & Nagar, A. K. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2937566 · Cited by 13 · see citing works
Incremental Adaptive EEG Classification of Motor Imagery-based BCI
Rong, H.-J., Li, C., Bao, R.-J., & Chen, B. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489283 · Cited by 13 · see citing works
Robbins, K. A., Su, K., & Hairston, W. D. (2017) · Data in Brief · doi:10.1016/j.dib.2017.11.032 · Cited by 13 · see citing works
Albuquerque, I., Monteiro, J., Rosanne, O., & Falk, T. H. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.992732 · Cited by 12 · see citing works
A coloring and timing brain-computer interface for the nursing bed robot
Li, Y., Zheng, T., Wang, M., Dong, L., Wang, P., & Qin, X. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107415 · Cited by 12 · see citing works
Albuquerque, I., Monteiro, J., Rosanne, O., Tiwari, A., Gagnon, J.-F., & Falk, T. H. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914469 · Cited by 12 · see citing works
Dual filtration subdomain adaptation network for cross-subject EEG emotion recognition
She, Q., Li, Y., Chen, Y., Meng, M., Liu, S., & Zhang, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130254 · Cited by 11 · see citing works
Study of the Length of time Window in Emotion Recognition based on EEG Signals
Sur, U. D. L. S., Silva, A. J., Pérez, V. A. G., Sur, U. D. L. S., Domínguez-Ramírez, O. A., & de Hidalgo, U. A. D. E. (2024) · Revista Mexicana de Ingeniería Biomédica · doi:10.17488/rmib.45.1.3 · Cited by 10 · see citing works
A Novel Doubly Reweighting Multisource Transfer Learning Framework
Ji, D., Jiang, Y., Qian, P., & Wang, S. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2868326 · Cited by 9 · see citing works
Megalingam, R. K., Sudheesh, S. K., & Manoharan, S. K. (2024) · Sensors · doi:10.3390/s24237690 · Cited by 8 · see citing works
Savić, A. M., Novicic, M. M., Miler-Jerković, V., Djordjević, O. C., & Konstantinović, L. M. (2024) · Biosensors · doi:10.3390/bios14080368 · Cited by 8 · see citing works
Online learning using projections onto shrinkage closed balls for adaptive brain-computer interface
Ma, Z., Cheng, J. S., & Tao, D. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107017 · Cited by 8 · see citing works
Finding the Optimal Cross-Subject EEG Data Alignment Method for Analysis and BCI
Bigdely-Shamlo, N., Ibagon, G., Kothe, C. A., & Mullen, T. R. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00196 · Cited by 8 · see citing works
Sequential Unsupervised Domain Adaptation through Prototypical Distributions.Preprint
Rostami, M. G., & Galstyan, A. (2020) · arXiv (Cornell University) · Cited by 8 · see citing works
Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces
He, H., & Wu, D. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914414 · Cited by 6 · see citing works
Self-citationMortier, S., Turkeš, R., Winne, J. D., Ransbeeck, W. V., Botteldooren, D. B., Devos, P., Latré, S., Leman, M., & Verdonck, T. (2023) · Sensors · doi:10.3390/s23239588 · Cited by 5 · see citing works
P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection
Song, X., Zeng, Y., Tong, L., Shu, J., Bao, G., & Yan, B. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.685173 · Cited by 5 · see citing works
Transfer Learning for Brain-Computer Interfaces: A Complete PipelinePreprint
Wu, D., Peng, R., Huang, J., & Zeng, Z. (2020) · arXiv (Cornell University) · Cited by 5 · see citing works
Self-citationUnsupervised Model Adaptation for Continual Semantic Segmentation
Stan, S., & Rostami, M. G. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16362 · Cited by 4 · see citing works
McDaniel, J. R., Gordon, S. M., & Lawhern, V. J. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283276 · Cited by 4 · see citing works
Source-free domain adaptation for semantic image segmentation using internal representations
Stan, S., & Rostami, M. G. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1359317 · Cited by 4 · see citing works
Cai, Z., Gao, Y., Fang, F. Z., Zhang, Y., & Du, S. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110332 · Cited by 5 · see citing works
Guo, J., & Huang, Z. (2021) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei53629.2021.9624451 · Cited by 4 · see citing works
Adaptive KF-SVM Classification for Single Trial EEG in BCI
Yang, B., Fan, C., Jia, J., Chen, S., & Wang, J. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-6370-1_4 · Cited by 4 · see citing works
A survey of neural signal decoding based on domain adaptation
Li, S., Tang, Z., Li, M., Yang, L., & Shang, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131653 · Cited by 3 · see citing works
Ding, Y., Li, L., & Li, Q. (2024) · IEEE Access · doi:10.1109/access.2024.3467154 · Cited by 3 · see citing works
Domain Agnostic Prototypical Distribution for Unsupervised Model AdaptationPreprint
Rostami, M. G., & Galstyan, A. (2020) · arXiv (Cornell University) · Cited by 3 · see citing works
Gao, Y., Ma, Y., Liu, Y., Yin, G., & Qin, Y. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10427-1 · Cited by 2 · see citing works
Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI
Xu, Y., Yin, H., Yi, W., Huang, X., Jian, W., Wang, C., & Hu, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1603104 · Cited by 2 · see citing works
User-wise perturbations for user identity protection in EEG-based BCIs
Chen, X., Li, S., Tu, Y., Wang, Z., & Wu, D. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad88a5 · Cited by 2 · see citing works
Self-citationActive Learning Approach for EEG Classification using Neural Networks: A review
Sebek, J., Schaabová, H., & Krajča, V. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8970017 · Cited by 2 · see citing works
Heterogeneous domain adaptation for intracortical signal classification using domain consensus
Dong, Y., Hu, D., Wang, S., & He, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104540 · Cited by 2 · see citing works
A Novel Hybrid Brain–Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli
Zhang, C., Li, G., Wu, X., & Gao, X. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3591616 · Cited by 1 · see citing works
Decoding saccadic eye movements from brain signals using an endovascular neural interface
Rasheed, S., Bennett, J. D., Yoo, P. E., Burkitt, A. N., & Grayden, D. B. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae0f52 · Cited by 1 · see citing works
SDDA: Spatial Distillation Based Distribution Alignment for Cross-Headset EEG Classification
Liu, D., Li, S., Wang, Z., Li, W., & Wu, D. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3631604 · Cited by 1 · see citing works
Self-citationHan, J.-W., Shin, D.-H., Ji, C.-H., Lee, D.-J., Kim, J.-M., Choi, W., Oh, J.-H., Cho, S., Son, Y.-H., & Kam, T. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114482 · Cited by 1 · 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
“Domain adaptation and domain generalization methods address cross-subject distribution shift in ERP-based BCIs [20–22], but are not designed for interpretability.”
- Authors
- Wu, D.
- Corresponding author
- Wu, Dongrui
- Venue
- IEEE Transactions on Human-Machine Systems
- Published
- 2017-08
- Volume
- 47
- Issue
- 4
- Pages
- 550-563
- ISSN
- 2168-2291 (ISSN-L)
- 2168-2305
- OpenAlex
- W2527330869
- Cited by
- 114
- References
- 51
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 15 · 2021: 18 · 2020: 21 · 2019: 17 · 2018: 4 · 2017: 7
- FWCI
- 5.95
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Human-Machine Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Army Research Laboratory (W911NF-10-2-0022, W911NF-10-D-0002/TO 0023)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- brain-computer interface, domain adaptation, BCI calibration, event-related potentials, EEG, online adaptation, single-trial classification, oddball paradigm, source domain selection, real-time applications
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