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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Open accessAbstract
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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“Notably, even for identical events, EEG signals exhibit considerable variability among different subjects, amplifying the difficulty in extracting pertinent information relevant to specific tasks [14].”
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“LDA aims to maximize the separation between different classes and minimize within-class scatter, making different class data more easily distinguishable in the feature space (Lotte et al. 2018).”
Eder, M., Xu, J., & Grosse‐Wentrup, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad6793 · Cited by 11
“The main source used as a reference was ‘A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update’ [8].”
Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy
Guerrero-Méndez, C. D., Blanco-Díaz, C. F., Rivera-Flor, H., Badue, C. S., Souza, A. F. D., Delisle-Rodríguez, D., & Bastos-Filho, T. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106837 · Cited by 8
Gulyás, D., & Jochumsen, M. (2024) · Sensors · doi:10.3390/s24186004 · Cited by 9
“The RF is an ensemble classifier that creates several decision trees based on a subset of the available features, and it works well on small training sets [35].”
BackgroundMethodVishnu, K. N., & Gupta, C. N. (2024) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ad8530 · Cited by 11
“Notwithstanding some sparse success [12], DNNs currently perform inferior to the current state-of-the-art SVM classifiers in CWL estimation [8, 13].”
A framework for Interpretable deep learning in cross-subject detection of event-related potentials
Jalilpour, S., & Müller-Putz, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109642 · Cited by 9
The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces
Wu, C., Yao, B., Zhang, X., et al. (2025) · Brain Sciences · doi:10.3390/brainsci15020168 · Cited by 8
“CSP extracts feature vectors from SMRs, which are then input into various classifiers for training and classification [10].”
Wang, H., Guo, J., Zhang, Y., Fu, Z. Q., & Yao, Y. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01617-9 · Cited by 8
“EEG characteristics are highly influenced by various individual differences such as age and psychology [164, 165].”
Behera, B. K., Al-Kuwari, S. M., & Farouk, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3572852 · Cited by 17
“Traditional classifiers have been frequently used for EEG-based BCI applications due to their simplicity and effectiveness in handling small datasets [10].”
An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification
Pritchard, M., Campelo, F., & Goldingay, H. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ade1f9 · Cited by 8
“Such ‘ biased comparisons, with [ ... ] unjustified choices of parameters [which prevent] us from ruling out manual tuning of these parameters with knowledge of the test set ’ [11] highlight the need for fair, unbiased explorations of candidate classification approaches.”
InfluentialSynergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces
Wu, C., Chen, N., Sun, T., Tan, P., Wang, P., & Li, G. (2025) · Brain‐X · doi:10.1002/brx2.70035 · Cited by 8
“[38,45] Continuous user feedback can provide personalized parameter settings and adaptive regulation for the system such that the BCI can more accurately capture and interpret user intentions and reduce the error rate (Figure 1C).”
Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm
Li, C., Li, N., Qiu, Y., Peng, Y., Wang, Y., Deng, L., Ma, T., Li, F., Yao, D., & Xu, P. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.01.002 · Cited by 8
Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry
Song, Z., Deng, B., Zhu, Y., Cai, L., Wang, J., & Yi, G. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-022-08118-7 · Cited by 8
Xu, T. B., Zhao, T., Cruz-Garza, J. G., Bhattacharjee, T., & Kalantari, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24670-8_28 · Cited by 8
The BciAi4SLA Project: Towards a User-Centered BCI
Cristina Gena, A., Hilviu, D., Chiarion, G., Roatta, S., Bosco, F. M., Calvo, A., Mattutino, C., & Vincenzi, S. (2023) · Electronics · doi:10.3390/electronics12051234 · Cited by 9
Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface
Perpetuini, D., Günal, M., Chiou, N., et al. (2023) · Bioengineering · doi:10.3390/bioengineering10050553 · Cited by 8
“The EEG evaluates the macroscopic dynamics of brain electrical activity via passive voltage measures on the scalp, with information characterized by low spatial resolution (of the order of centimeters) but high temporal resolution (of the order of milliseconds) [5], which is a great advantage for real-time BCI applica…”
BackgroundState-of-the-art mental tasks classification based on electroencephalograms: a review
Saini, M., & Satija, U. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acd51b · Cited by 8
Wei, Y., Wang, X., Luo, R., Mai, X., Li, S., & Meng, J. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad01de · Cited by 8
“The RBF-SVM is widely used and has been proven to be one of the most effective classifiers in EEG-based motor decoding problems [34, 35].”
Sengupta, P., & Lakshminarayanan, K. (2023) · Behavioural Brain Research · doi:10.1016/j.bbr.2023.114760 · Cited by 8
Ohkubo, M. (2023) · Review of Scientific Instruments · doi:10.1063/5.0167372 · Cited by 8
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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