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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
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Objective . Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach . We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results . We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance . This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.
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“If the data dimension is too large, the Riemannian Geometry method will perform many calculations, which is time-consuming and causes statistical deviations [22].”
From Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier (2020) · cites this paper as [22]
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“xDAWN spatial filtering [13] with Riemannian geometry (xDAWN+RG) features also perform well cross-subject [14], [15] but lack obvious physiological interpretation [8].”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [8]
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“General-purpose deep learning architectures such as EEGNet [6], [7], [8] have been evaluated across multiple BCI paradigms”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [8]
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,715
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
(2011) · Cited by 6,993
2009 IEEE Conference on Computer Vision and Pattern Recognition
(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,646
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
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Deep 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
Optimization Algorithms on Matrix Manifolds
Absil, P.-A., Mahony, R. E., & Sepulchre, R. J. (2008) · Princeton University Press eBooks · doi:10.1515/9781400830244 · Cited by 3,715
Electroencephalography—Basic principles, clinical applications and related fields
(1982) · Journal of the Autonomic Nervous System · doi:10.1016/0165-1838(82)90067-4 · Cited by 3,449
A well-conditioned estimator for large-dimensional covariance matrices
Ledoit, O., & Wolf, M. (2004) · Journal of Multivariate Analysis · doi:10.1016/s0047-259x(03)00096-4 · Cited by 3,046
The Geometry of Algorithms with Orthogonality Constraints
Edelman, A. S., Arias, T. A., & Smith, S. T. (1998) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479895290954 · Cited by 2,741
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,739
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665 · cited 13×
“We thus advise our readers new to the EEG classification field to start by reading [141], as that paper is more accessible, and the concepts it presented will not be explained again in the current manuscript.”
BackgroundInfluentialSelf-citationTsoumakas, G., & Katakis, I. (2007) · International Journal of Data Warehousing and Mining · doi:10.4018/jdwm.2007070101 · Cited by 2,512
Optimal spatial filtering of single trial EEG during imagined hand movement
Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247911 · Cited by 2,278
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 2×
“Then, EEG signals are typically preprocessed using various spatial and spectral filters [23], and features are extracted from these signals in order to represent them in a compact form [140].”
MethodTransfer Feature Learning with Joint Distribution Adaptation
Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.274 · Cited by 2,071
Electroencephalography: Basic Principles, Clinical Applications and Related Fields
Torres, F. (1983) · Archives of Neurology · doi:10.1001/archneur.1983.04050030085025 · Cited by 1,825
Markov Random Field Modeling in Image Analysis
Li, S. Z. (2001) · Computer science workbench · doi:10.1007/978-4-431-67044-5 · Cited by 1,547
Frustratingly Easy Domain AdaptationPreprint
Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,535
Pattern Recognition and Machine Learning
Maindonald, J. H. (2007) · Journal of Statistical Software · doi:10.18637/jss.v017.b05 · Cited by 1,421
Nonnegative Matrix and Tensor Factorizations
Cichocki, A. S., Zdunek, R., Phan, A. H., & Амари, Ш.-И. (2009) · Wiley eBooks · doi:10.1002/9780470747278 · Cited by 1,404
Self-citationCichocki, A. S., Mandic, D. P., Lathauwer, L. D., Zhou, G., Zhao, Q., Caiafa, C. F., & Phan, A. H. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2297439 · Cited by 1,392
Self-citationFilter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00039 · Cited by 1,264
Single-trial analysis and classification of ERP components — A tutorial
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138
Brain–Computer InterfacesPrinciples and Practice
Wolpaw, J. R., & Wolpaw, E. W. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388855.001.0001 · Cited by 1,134
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117
Learning mid-level features for recognition
Boureau, Y.-L., Bach, F., LeCun, Y., & Ponce, J. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539963 · Cited by 1,091
Bhatia, R. (2009) · Princeton University Press eBooks · doi:10.1515/9781400827787 · Cited by 1,059
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
Self-citationOptimal Transport for Domain Adaptation
Courty, N., Flamary, R., Tuia, D., & Rakotomamonjy, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2615921 · Cited by 1,027
Self-citationNeocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
Fukushima, K., & Miyake, S. (1982) · Lecture Notes in Biomathematics · doi:10.1007/978-3-642-46466-9_18 · Cited by 991
Zander, T. O., & Kothe, C. A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025005 · Cited by 947
Spatial filter selection for EEG-based communication
McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937
Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r03 · Cited by 907
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
Self-citationA novel deep learning approach for classification of EEG motor imagery signals
Tabar, Y. R., & Halici, U. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016003 · Cited by 859
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390290 · Cited by 847
Markov Random Field Modeling in Image Analysis
Li, S. Z. (2009) · Advances in pattern recognition · doi:10.1007/978-1-84800-279-1 · Cited by 830
Toward Brain-Computer Interfacing
Dornhege, G., Millán, J. D. R., Hinterberger, T., McFarland, D. J., & Müller, K.-R. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.001.0001 · Cited by 789
Induction of decision trees. Machine Learning
Quinlan, J. R. (1986) · Symposium on the Theory of Computing · Cited by 783
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778
A comparison of classification techniques for the P300 Speller
Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762
Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG
Millán, J. D. R., Frédéric Renkens, B., Mourino, J., & Wulfram Gerstner, B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827086 · Cited by 753
An extensive experimental comparison of methods for multi-label learning
Madjarov, G., Kocev, D., Gjorgjevikj, D., & Džeroski, S. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.03.004 · Cited by 728
Direct importance estimation with model selection and its application to covariate shift adaptation
Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P., & Kawanabe, M. (2008) · Cited by 681
Dornhege, G., Blankertz, B., Curio, G., & Müller, K. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827088 · Cited by 638
An Introduction to Restricted Boltzmann Machines
Fischer, A., & Igel, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33275-3_2 · Cited by 605
LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046003 · Cited by 583
xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface
Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2012869 · Cited by 537
Support Vector Channel Selection in BCI
Lal, T., Schroder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., & Schölkopf, B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827827 · Cited by 516
Classification of covariance matrices using a Riemannian-based kernel for BCI applications
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.12.039 · Cited by 493
Self-citationNoninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks
Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Scientific Reports · doi:10.1038/srep38565 · Cited by 484
Bregman Divergence-Based Regularization for Transfer Subspace Learning
Si, S., Tao, D., & Geng, B. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.126 · Cited by 482
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
Cichocki, A. S., Lee, N., Oseledets, I. V., Phan, A. H., Zhao, Q., & Mandic, D. P. (2016) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000059 · Cited by 456
Self-citationRiemannian geometry for EEG-based brain-computer interfaces; a primer and a review
Congedo, M., Barachant, A., & Bhatia, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1297192 · Cited by 456
Self-citationTowards adaptive classification for BCI
Shenoy, P., Krauledat, M., Blankertz, B., Rao, R. P. N., & Müller, K. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/1/r02 · Cited by 448
An up-to-date comparison of state-of-the-art classification algorithms
Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines
Lu, N., Li, T., Ren, X., & Miao, H. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2601240 · Cited by 422
Riemannian Approaches in Brain-Computer Interfaces: A Review
Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420
Self-citationRecognition of emotions using multimodal physiological signals and an ensemble deep learning model
Yin, Z., Zhao, M., Wang, Y., Yang, J., & Zhang, J. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.005 · Cited by 416
Transfer 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
Self-citationCombined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing
Dornhege, G., Blankertz, B., Krauledat, M., Losch, F., Curio, G., & Müller, K. R. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.883649 · Cited by 400
Interpretable deep neural networks for single-trial EEG classification
Sturm, I., Lapuschkin, S., Samek, W., & Müller, K. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.10.008 · Cited by 396
Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG
Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., & Jung, T. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609968 · Cited by 391
Clerc, M., Bougrain, L., & Lotte, F. (2010) · Human-computer interaction series · doi:10.1007/978-1-84996-272-8 · Cited by 391
Self-citationCompetition and Cooperation in Neural Nets
Амари, Ш.-И., & Arbib, M. A. (1977) · Elsevier eBooks · doi:10.1016/b978-0-12-491850-4.50009-x · Cited by 378
Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction
Ferrez, P. W., & Millán, J. D. R. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.908083 · Cited by 365
FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A. (2013) · International Journal of Neural Systems · doi:10.1142/s0129065714500130 · Cited by 363
Self-citationBrain-Computer Interfaces: Beyond Medical Applications
van Erp, J., Lotte, F., & Tangermann, M. (2012) · Computer · doi:10.1109/mc.2012.107 · Cited by 362
Self-citationThe wadsworth BCI research and development program: at home with BCI
Vaughan, T. M., McFarland, D. J., Schalk, G., Sarnacki, W. A., Krusienski, D. J., Sellers, E. W., & Wolpaw, J. R. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875577 · Cited by 340
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327
Self-citationHerman, P. A., Prasad, G., McGinnity, T. M., & Coyle, D. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.926694 · Cited by 322
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307
Self-citationGraimann, B., Allison, B. Z., & Pfurtscheller, G. (2010) · The frontiers collection · doi:10.1007/978-3-642-02091-9 · Cited by 304
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces
Vidaurre, C., Kawanabe, M., von Bünau, P., Blankertz, B., & Müller, K. R. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2093133 · Cited by 296
Kwak, N.-S., Müller, K., & Lee, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172578 · Cited by 276
L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI
Zhang, Y., Zhou, G., Jin, J., Wang, M., Wang, X., & Cichocki, A. S. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2279680 · Cited by 267
Self-citationZhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. S. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.004 · Cited by 266
Self-citationSubject-independent mental state classification in single trials
Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K., & Grozea, C. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.003 · Cited by 265
Composite Common Spatial Pattern for Subject-to-Subject Transfer
Kang, H., Nam, Y., & Choi, S. (2009) · IEEE Signal Processing Letters · doi:10.1109/lsp.2009.2022557 · Cited by 265
Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysisPreprint
Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. S. (2014) · Cited by 262
Self-citationCichocki, A. S., Phan, A.-H., Zhao, Q., Lee, N., Oseledets, I. V., Sugiyama, M., & Mandic, D. P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000067 · Cited by 243
Self-citationKübler, A., Holz, E. M., Riccio, A., Zickler, C., Kaufmann, T., Kleih, S. C., Staiger-Sälzer, P., Desideri, L., Hoogerwerf, E.-J., & Mattia, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112392 · Cited by 236
Li, Y., Guan, C., Li, H., & Chin, Z. Y. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.01.030 · Cited by 230
Ica: a potential tool for bci systems
Kachenoura, A., Albera, L., Senhadji, L., & Comon, P. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408442 · Cited by 226
Zhou, S., Gan, J. Q., & Sepulveda, F. (2007) · Information Sciences · doi:10.1016/j.ins.2007.11.012 · Cited by 223
Rehabilitation with Brain-Computer Interface Systems
Pfurtscheller, G., Müller-Putz, G., Scherer, R., & Neuper, C. (2008) · Computer · doi:10.1109/mc.2008.432 · Cited by 223
EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies
Royer, A. S., Doud, A. J., Rose, M. L., & He, B. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2077654 · Cited by 223
On the need for on-line learning in brain-computer interfaces
Millán, J. D. R. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1381116 · Cited by 215
Brain-Computer Interface in Stroke Rehabilitation
Ang, K. K., & Guan, C. (2013) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2013.7.2.139 · Cited by 214
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing
Blankertz, B., Kawanabe, M., Tomioka, R., Hohlefeld, F. U., Nikulin, V. V., & Müller, K. R. (2008) · Cited by 214
Tensor decompositions for feature extraction and classification of high dimensional datasets
Phan, A. H., & Cichocki, A. S. (2010) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.1.37 · Cited by 212
Self-citationMachine-learning-based coadaptive calibration for brain-computer interfaces
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Neural Computation · doi:10.1162/neco_a_00089 · Cited by 207
Lotte, F. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6584-2_7 · Cited by 190
Self-citationImpossibility Theorems for Domain Adaptation
Ben-David, S., Lu, T., Luu, T. V., & Pál, D. (2010) · Cited by 189
EEG feature comparison and classification of simple and compound limb motor imagery
Yi, W., Qiu, S., Qi, H., Zhang, L., Wan, B., & Ming, D. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-106 · Cited by 185
Towards a robust BCI: error potentials and online learning
Buttfield, A., Ferrez, P. W., & Millán, J. D. R. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875555 · Cited by 181
Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI
Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2189584 · Cited by 180
Divergence-Based Framework for Common Spatial Patterns Algorithms
Samek, W., Kawanabe, M., & Müller, K. (2013) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2290621 · Cited by 179
Deep Extreme Learning Machine and Its Application in EEG Classification
Ding, S., Zhang, N., Xu, X., Guo, L., & Zhang, J. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/129021 · Cited by 175
Spüler, M., Rosenstiel, W., & Bogdan, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051077 · Cited by 175
EEG-based workload estimation across affective contexts
hl, C. M. ⁄., Jeunet, C., & Lotte, F. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00114 · Cited by 175
Self-citationMultiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs
Zhang, Y., Zhou, G., Zhao, Q., Onishi, A., Jin, J., Wang, X., & Cichocki, A. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24955-6_35 · Cited by 173
Self-citationCo-adaptive calibration to improve BCI efficiency
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025009 · Cited by 169
A regularized discriminative framework for EEG analysis with application to brain–computer interface
Tomioka, R., & Müller, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.07.045 · Cited by 169
Critical issues in state-of-the-art brain–computer interface signal processing
Krusienski, D. J., Grosse‐Wentrup, M., Galán, F., Coyle, D., Miller, K. J., Forney, E. M., & Anderson, C. W. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025002 · Cited by 169
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 164
Towards Practical Brain-Computer Interfaces
Allison, B. Z., Dunne, S., Leeb, R., Millán, J. D. R., Allison, B. Z., Dunne, S., Leeb, R., Millán, J. D. R., & Nijholt, A. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5 · Cited by 163
Cross-session classification of mental workload levels using EEG and an adaptive deep learning model
Yin, Z., & Zhang, J. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.11.013 · Cited by 163
Classification by Pairwise Coupling
Hastie, T., & Tibshirani, R. J. (1997) · Cited by 158
Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification
Higashi, H., & Tanaka, T. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2215960 · Cited by 155
Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling
Perrin, M., Maby, E., Daligault, S., Bertrand, O., & Mattout, J. (2012) · Advances in Human-Computer Interaction · doi:10.1155/2012/578295 · Cited by 149
Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces
Vidaurre, C., Schlögl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.888836 · Cited by 144
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Manor, R., & Geva, A. B. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00146 · Cited by 144
Evaluation Criteria for BCI Research
Schlögl, A., Kronegg, J., Huggins, J. E., & Mason, S. G. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.003.0024 · Cited by 141
Kamousi, B., Liu, Z., & He, B. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.847386 · Cited by 140
Noninvasive BCIs: Multiway Signal-Processing Array Decompositions
Cichocki, A. S., Washizawa, Y., Rutkowski, T. M., Bakardjian, H., Phan, A. H., Choi, S., Lee, H., Zhao, Q., Zhang, L., & Li, Y. (2008) · Computer · doi:10.1109/mc.2008.431 · Cited by 136
Self-citationSteyrl, D., Scherer, R., Faller, J., & Müller-Putz, G. (2016) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2014-0117 · Cited by 133
Efficient mental workload estimation using task-independent EEG features
Roy, R. N., Charbonnier, S., Campagne, A., & Bonnet, S. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/2/026019 · Cited by 129
Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets
Fatourechi, M., Ward, R. K., Mason, S. G., Huggins, J. E., Schlögl, A., & Birch, G. E. (2008) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2008.34 · Cited by 128
Unsupervised Brain Computer Interface Based on Intersubject Information and Online Adaptation
Lu, S., Guan, C., & Zhang, H. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2015197 · Cited by 122
Transfer Learning in Brain-Computer InterfacesPreprint
Jayaram, V., Alamgir, M., Altün, Y., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.00296 · Cited by 119
An analysis of performance evaluation for motor-imagery based BCI
Thomas, E. M., Dyson, M., & Clerc, M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/031001 · Cited by 116
Self-citationHazrati, M. K., & Erfanian, A. (2010) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2010.04.016 · Cited by 114
Learning a common dictionary for subject-transfer decoding with resting calibration
Morioka, H., Kanemura, A., Hirayama, J.-I., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., & Ishii, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.015 · Cited by 113
Electroencephalography ( EEG )‐Based Brain–Computer Interfaces
Lotte, F., Bougrain, L., & Clerc, M. (2015) · Wiley eBooks · doi:10.1002/047134608x.w8278 · Cited by 111
Self-citationAn efficient word typing P300-BCI system using a modified T9 interface and random forest classifier
Akram, F., Han, S. M., & Kim, T. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.10.021 · Cited by 107
Online SSVEP-based BCI using Riemannian geometry
Kalunga, E. K., Chevallier, S., Barthélemy, Q., Djouani, K., Monacelli, É., & Hamam, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.007 · Cited by 104
Wei, Q., Wang, Y., Gao, X., & Gao, S. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/012 · Cited by 103
A Plug&Play P300 BCI Using Information Geometry
Barachant, A., & Congedo, M. (2014) · arXiv · doi:10.48550/arxiv.1409.0107 · Cited by 102
Self-citationNeurofeedback Training for BCI Control
Neuper, C., & Pfurtscheller, G. (2009) · The Frontiers Collection · doi:10.1007/978-3-642-02091-9_4 · Cited by 102
Guide to Brain-Computer Music Interfacing
(2014) · doi:10.1007/978-1-4471-6584-2 · Cited by 100
Kindermans, P.-J., Tangermann, M., Müller, K., & Schrauwen, B. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035005 · Cited by 98
Comparative study of band-power extraction techniques for Motor Imagery classification
Brodu, N., Lotte, F., & Lécuyer, A. (2011) · IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) · doi:10.1109/ccmb.2011.5952105 · Cited by 98
Self-citationHeading for new shores! Overcoming pitfalls in BCI design
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1263916 · Cited by 97
Self-citationAdaptive BCI Based on Variational Bayesian Kalman Filtering: An Empirical Evaluation
Sykacek, P., Roberts, S., & Stokes, M. J. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.824128 · Cited by 93
Clerc, M., Bougrain, L., & Lotte, F. (2016) · Wiley eBooks · doi:10.1002/9781119144977 · Cited by 93
Self-citationClassification of movement intention by spatially filtered electromagnetic inverse solutions
Congedo, M., Lotte, F., & Lécuyer, A. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/8/002 · Cited by 92
Self-citationBayesian common spatial patterns for multi-subject EEG classification
Kang, H., & Choi, S. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.05.012 · Cited by 91
Multitask Learning for Brain-Computer Interfaces
Alamgir, M., Grosse‐Wentrup, M., & Altün, Y. (2010) · Cited by 90
EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface
Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. H. (2013) · Neural Computation · doi:10.1162/neco_a_00474 · Cited by 88
True Zero-Training Brain-Computer Interfacing – An Online Study
Kindermans, P.-J., Schreuder, M., Schrauwen, B., Müller, K., & Tangermann, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102504 · Cited by 87
Höhne, J., Holz, E., Staiger-Sälzer, P., Müller, K.-R., Kübler, A., & Tangermann, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104854 · Cited by 85
Brain-Computer Interfaces Handbook: Technological and Theoretical Advances
Nam, C. S., Nijholt, A., & Lotte, F. (2018) · University of Twente Research Information · Cited by 83
Self-citationDecoding Finger Flexion from Band-Specific ECoG Signals in Humans
Liang, N., & Bougrain, L. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00091 · Cited by 82
Self-citationNicolas-Alonso, L. F., Corralejo, R., Gómez‐Pilar, J., Álvarez, D., & Hornero, R. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.005 · Cited by 80
Waytowich, 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
The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing
Ma, T., Li, Y., Yang, H., Lv, X., Li, P., Liu, T., Yao, D., & Xu, P. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.11.002 · Cited by 78
Adaptive Classification for Brain Computer Interfaces
Blumberg, J., Rickert, J., Waldert, S., Schulze‐Bonhage, A., Aertsen, A., & Mehring, C. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352845 · Cited by 77
Krusienski, D. J., McFarland, D. J., & Wolpaw, J. R. (2011) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2011.09.019 · Cited by 75
Single-Trial EEG Source Reconstruction for Brain–Computer Interface
Noirhomme, Q., Kitney, R. I., & Macq, B. M. M. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.913986 · Cited by 75
Gbureck, U., Lansnicker, L., Holste, T., & Thulĺ, R. (2004) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt.2004.003 · Cited by 73
Common Spatial Pattern revisited by Riemannian geometry
Barachant, A., Bon, S., Congedo, M., & Christian Jutten, B. (2010) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2010.5662067 · Cited by 72
Self-citationLi, Y., & Guan, C. (2007) · Machine Learning · doi:10.1007/s10994-007-5039-1 · Cited by 70
On the use of interaction error potentials for adaptive brain computer interfaces
Llera, A., van Gerven, M. A. J., Gómez, V., et al. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.05.006 · Cited by 69
Meng, J., Yao, L., Sheng, X., Zhang, D., & Zhu, X. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2345458 · Cited by 67
Towards improved BCI based on human learning principles
Lotte, F., & Jeunet, C. (2015) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2015.7073024 · Cited by 67
Self-citationFeature Selection for Brain-Computer Interfaces
Koprinska, I. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14640-4_8 · Cited by 66
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach
Krauledat, M., Schröder, M., Blankertz, B., & Müller, K. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0099 · Cited by 66
A mathematical model for the two-learners problem
Müller, J. S., Vidaurre, C., Schreuder, M., Meinecke, F. C., von Bünau, P., & Müller, K. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa620b · Cited by 66
Brodu, N., Lotte, F., & Lécuyer, A. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.10.010 · Cited by 65
Self-citationShould the parameters of a BCI translation algorithm be continually adapted?
McFarland, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.04.037 · Cited by 62
Adaptation in P300 Brain–Computer Interfaces: A Two-Classifier Cotraining Approach
Panicker, R. C., Puthusserypady, S. K., & Sun, Y. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2058804 · Cited by 61
Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition
Zhang, Y., Zhou, G., Jin, J., Zhang, Y., Wang, X., & Cichocki, A. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.11.008 · Cited by 61
Self-citationAn Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials
Wöhrle, H., Krell, M. M., Straube, S., Kim, S. K., Kirchner, E. A., & Kirchner, F. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2402252 · Cited by 60
Tensor Decompositions: A New Concept in Brain Data Analysis?Preprint
Cichocki, A. S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.0395 · Cited by 60
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Hoffmann, U., Vésin, J.-M., & Ebrahimi, T. (2006) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 59
Geometry-aware principal component analysis for symmetric positive definite matrices
Horev, I., Yger, F., & Sugiyama, M. (2016) · Machine Learning · doi:10.1007/s10994-016-5605-5 · Cited by 58
Self-citationCovariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Zhang, K., Zheng, V. W., Wang, Q., Kwok, J. T.-Y., Yang, Q., & Marsic, I. (2013) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 57
Lotte, F., Lécuyer, A., & Arnaldi, B. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2020752 · Cited by 57
Self-citationHsu, W. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.05.014 · Cited by 56
A co-adaptive brain-computer interface for end users with severe motor impairment
Faller, J., Scherer, R., Costa, U., Opisso, E., Medina, J., & Müller-Putz, G. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101168 · Cited by 55
DUF581 Is Plant Specific FCS-Like Zinc Finger Involved in Protein-Protein Interaction
Muhammed Jamsheer, K., & Laxmi, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099074 · Cited by 55
Classification of biological signals using linear and nonlinear features
Ballı, T., & Palaniappan, R. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/7/003 · Cited by 55
A subject-independent pattern-based Brain-Computer Interface
Ray, A. M., Sitaram, R., Rana, M., Pasqualotto, E., Buyukturkoglu, K., Guan, C., Ang, K. K., Cristián Tejos, N., Zamorano, F. J., Aboitiz, F., et al. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00269 · Cited by 55
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Sannelli, C., Vidaurre, C., Müller, K., & Blankertz, B. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046003 · Cited by 55
An Online Semi-supervised Brain–Computer Interface
Gu, Z., Yu, Z., Shen, Z., & Li, Y. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2261994 · Cited by 53
Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study
Yger, F., Lotte, F., & Sugiyama, M. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362879 · Cited by 50
Self-citation(2016) · Wiley eBooks · doi:10.1002/9781119332428 · Cited by 47
Hangman BCI: An unsupervised adaptive self-paced Brain–Computer Interface for playing games
Hasan, B. A. S., & Gan, J. Q. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.02.004 · Cited by 46
Flagged first, then by citations
5 of the 266 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Allwein, 2001, Reducing multiclass to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res., 1, 113
- Chevallier, 2018, Riemannian classification for SSVEP-based BCI, 10.1201/9781351231954-19 · DOI
- Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157
Cited by
Sorted by citations
Deep learning-based electroencephalography analysis: a systematic review
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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
Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., Bencherif, M. A., & Faisal, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06352-5 · Cited by 626
Rashid, M., Sulaiman, N., Majeed, A. P. P. A., Musa, R. M., Nasir, A. F. A., Bari, B. S., & Khatun, S. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00025 · Cited by 545
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Metadata
- Authors
- Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- RIKEN Center for Brain Science (JP)
- Skolkovo Institute of Science and Technology (RU)
- Université Côte d'Azur (FR)
- GIPSA-Lab (FR)
- Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (FR)
- Université Paris Sciences et Lettres (FR)
- Corresponding author
- Lotte, F
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2018-04-16
- Volume
- 15
- Issue
- 3
- Pages
- 031005
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2794345050
- Cited by
- 2,239
- References
- 266
- Citations by year
- 2026: 320 · 2025: 284 · 2024: 262 · 2023: 283 · 2022: 276 · 2021: 321 · 2020: 273 · 2019: 170 · 2018: 49
- FWCI
- 27.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine) (submittedVersion, cc-by-sa)
- Funders
- Polish National Science Center (2016/20/W/N24/00354)
- Ministry of Education and Science of the Russian Federation (14.756.31.0001)
- Agence Nationale de la Recherche (ANR-15-CE23-0013-01)
- Japan Society for the Promotion of Science
- H2020 European Research Council (ERC-2016-STG-714567)
- 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-based brain-computer interface, EEG classification, machine learning classifiers, adaptive classifier, unsupervised adaptation, transfer learning, deep learning, Riemannian geometry, random forest, small training samples
- MeSH terms
- Deep Learning, Algorithms, Animals, Brain, Electroencephalography, Humans, Signal Processing, Computer-Assisted, Time Factors, Brain-Computer Interfaces
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