Cited by 2,239EEG and Brain-Computer InterfacesEmotion and Mood RecognitionGaze Tracking and Assistive Technology

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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

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Lotte, F, Bougrain, L, Cichocki, A, Clerc, M, Congedo, M, Rakotomamonjy, A, Yger, F·2018·Journal of Neural Engineering

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Abstract

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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Random Forests
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
A survey on transfer learning
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
Induction of decision trees
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

Show all 266 references

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

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Szul, M. J., Papadopoulos, S., Alavizadeh, S., Daligaut, S., Schwartz, D., Mattout, J., & Bonaiuto, J. J. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102490 · Cited by 51
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Değirmenci, M., Yüce, Y. K., Perc, M., et al. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1223307 · Cited by 49
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Background
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Fernandes, J. V. M. R., de Alexandria, A. R., Marques, J. A. L., de Assis, D. F., Motta, P. C., & Silva, B. R. D. S. (2024) · Bioengineering · doi:10.3390/bioengineering11080782 · Cited by 50
“Despite the abovementioned challenges in obtaining such data, several public and private datasets have been instrumental in advancing the field [23].”
Background
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Zhang, R., Li, X., Wang, Y., Liu, B., Shi, L., Chen, M., Zhang, L., & Hu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2917327 · Cited by 49
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Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions
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Yuan, H., Li, Y., Yang, J., Li, H., Yang, Q.-Y., Guo, C., Zhu, S., & Shu, X. (2021) · Micromachines · doi:10.3390/mi12121521 · Cited by 51
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Ju, C., & Guan, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3307470 · Cited by 48
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Vavoulis, A., Figueiredo, P., & Vourvopoulos, A. (2023) · Signals · doi:10.3390/signals4010004 · Cited by 55
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Chiossi, F., Ou, C., Gerhardt, C., Putze, F., & Mayer, S. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103433 · Cited by 48
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Kosmyna, N., & Lécuyer, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210145 · Cited by 50
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Schneider, T., Wang, X., Hersche, M., Cavigelli, L., & Benini, L. (2020) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp50058.2020.00065 · Cited by 48
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Žabčíková, M., Koudelková, Z., Jašek, R., & Lorenzo-Navarro, J. (2021) · International Journal of Developmental Neuroscience · doi:10.1002/jdn.10166 · Cited by 50
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Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B. H., & Müller, J. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621225 · Cited by 49
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Zhang, X., She, Q., Chen, Y., Kong, W., & Mei, C. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106150 · Cited by 47
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Qu, X., Mei, Q., Liu, P., & Hickey, T. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60735-7_7 · Cited by 45
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Background
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Banville, H. J., Wood, S. U. N., Aimone, C., Engemann, D. A., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118994 · Cited by 45
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Said, R. R., Heyat, M. B. B., Song, K., Tian, C., & Wu, Z. (2022) · Biosensors · doi:10.3390/bios12121134 · Cited by 40
Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition
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Huang, E., Zheng, X., Fang, Y., et al. (2021) · IRBM · doi:10.1016/j.irbm.2021.04.004 · Cited by 44
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Labs, C.-L. A. R., Sussillo, D., Kaifosh, P., & Reardon, T. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.23.581779 · Cited by 44
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Miladinović, A., Ajčević, M., Jarmolowska, J., Marušič, U., Colussi, M., Silveri, G., Battaglini, P. P., & Accardo, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105808 · Cited by 41
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Tayeb, Z., Waniek, N., Fedjaev, J., Ghaboosi, N., Rychly, L., Widderich, C., Richter, C., Braun, J., Saveriano, M., Cheng, G., et al. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae186 · Cited by 42
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Self-citation
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Gao, C., Liu, W., & Yang, X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.024 · Cited by 41
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Ajmeria, R., Mondal, M., Banerjee, R., Halder, T., Deb, P. K., Mishra, D., Nayak, P., Misra, S., Pal, S. K., & Chakravarty, D. (2023) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2022.3232576 · Cited by 28
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Barthélemy, Q., Mayaud, L., Ojeda, D., & Congedo, M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2893113 · Cited by 39
Self-citation
Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system
Taheri, S., Ezoji, M., & Sakhaei, S. M. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-2378-z · Cited by 40
CNN With Large Data Achieves True Zero-Training in Online P300 Brain-Computer Interface
Lee, J., Won, K., Kwon, M., Jun, S. C., & Ahn, M. (2020) · IEEE Access · doi:10.1109/access.2020.2988057 · Cited by 40
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Roy, S., Dora, S., McCreadie, K. A., & Prasad, G. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206942 · Cited by 40
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Self-citation
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“Considerable progress has been made in this ﬁ eld as a result of previous notable review articles [33], [34], [35], [36], [37], [38], [39], [40], [41].”
Portable brain-computer interface based on novel convolutional neural network
Zhang, Y., Zhang, X., Sun, H., Fan, Z., & Zhong, X. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.023 · Cited by 39
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Functional disconnection of associative cortical areas predicts performance during BCI training
Corsi, M., Chávez, M., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S., & Fallani, F. D. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116500 · Cited by 43
Grand Challenges in Neurotechnology and System Neuroergonomics
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“Lotte et al. [53] suggested using three classifiers—a shrinkage LDA classifier, random forest, and Riemannian classifier—when confronted with scarce training samples.”
Method
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A review of EEG acquisition, processing and application
Li, B., Cheng, T., & Guo, Z. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1907/1/012045 · Cited by 38

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
ORCID: Lotte, F
Bougrain, L
Cichocki, A
Clerc, M
Congedo, M
Rakotomamonjy, A
Yger, 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
DOI: 10.1088/1741-2552/aab2f2
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: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
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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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

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