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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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Convolutional Neural Network-Based EEG Signal Analysis: A Systematic Review
Rajwal, S., & Aggarwal, S. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09920-1 · Cited by 97
“Furthermore, many surveys conducted [88, 89] also reported that most of the studies related to CNN-EEG began after 2014.”
BackgroundData augmentation for self-paced motor imagery classification with C-LSTM.
Freer, D., & Yang, G.-Z. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab57c0 · Cited by 96
Thinker invariance: enabling deep neural networks for BCI across more people
Kostas, D., & Rudzicz, F. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abb7a7 · Cited by 102
Pei, Y., Luo, Z., Yan, Y., Yan, H., Jiang, J., Li, W., Xie, L., & Yin, E. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.645952 · Cited by 96
Learning Invariant Representations From EEG via Adversarial Inference
Özdenizci, O., Wang, Y., Koike–Akino, T., & Deniz Erdogmus, B. (2020) · IEEE Access · doi:10.1109/access.2020.2971600 · Cited by 99
Evaluating Wayfinding Designs in Healthcare Settings through EEG Data and Virtual Response Testing
Kalantari, S., Tripathi, V., Kan, J., Rounds, J. D., Mostafavi, A., Snell, R., & Cruz-Garza, J. G. (2021) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2021.101744 · Cited by 107
Jiang, N., Chen, C., He, J., Meng, J., Pan, L., Su, S., & Zhu, X. (2023) · National Science Review · doi:10.1093/nsr/nwad048 · Cited by 103
“The common spatial pattern approach and its improved versions (such as the filter bank common spatial pattern [127]) were proved to be effective spatial filtering methods for obtaining the optimized weights of a large number of electrodes and solving a binary classification problem [128].”
BackgroundMethodA Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding
Ma, X., Chen, W., Pei, Z., Liu, J., Huang, B., & Chen, J. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3299355 · Cited by 94
“Furthermore, many advanced deep learning architectures are designed to compensate for the defect of conventional BCI systems and improve MI-EEG decoding performance [19].”
BackgroundTensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Ju, C., & Guan, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3172108 · Cited by 97
Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review
Khan, H., Naseer, N., Yazidi, A., Eide, P. K., Hassan, H. W., & Mirtaheri, P. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.613254 · Cited by 98
Li, M., He, D., Li, C., & Qi, S. (2021) · Brain Sciences · doi:10.3390/brainsci11040450 · Cited by 101
Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder
Ditthapron, A., Banluesombatkul, N., Ketrat, S., Chuangsuwanich, E., & Wilaiprasitporn, T. (2019) · IEEE Access · doi:10.1109/access.2019.2919143 · Cited by 92
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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