The DOI matches its Crossref record; 261 of the 266 references it cites were found in a registry or an index.
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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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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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5 of the 266 references couldn’t be matched to a known paper.
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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
“General-purpose deep learning architectures such as EEGNet [6–8] have been evaluated across multiple BCI paradigms; CNN-transformer architectures have demonstrated competitive cross-subject ErrP performance under leave-one-subject-out (LOSO) evaluation [ 9, 10]; and dense convolutional transformers…”
Informing EEG-Based Error Decoding With Explainable AI
Jeremy Chan, H. T., Wimmer, M., Šimić, I., Müller-Putz, G. R., & Veas, E. (2025) · 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc58881.2025.11343575
Zhang, Q., & Liu, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.07108 · Cited by 57
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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