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A review of classification algorithms for EEG-based brain–computer interfaces
Open accessAbstract
In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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“each paradigm relies on manual specification of signal processing [7], feature extraction [8] and classification methods [9], a process which often requires significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.”
From EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces (2018) · cites this paper as [9]
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“Third, SVM is known to have good generalization properties and to be insensitive to overtraining and to the curse-of-dimensionality, especially in the small data set [25].”
From Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition (2017) · cites this paper as [25]
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“The continuous development of EEG-BCI applications puts forward higher requirements for the processes of BCI, where the reliance on human expertise is a necessary problem to be solved [16].”
From IENet: a robust convolutional neural network for EEG based brain-computer interfaces (2022) · cites this paper as [16]
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EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
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Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887
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
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
“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-citationBrain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
Metadata
- Authors
- Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Orange (France) (FR)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2007-01-31
- Volume
- 4
- Issue
- 2
- Pages
- R1-R13
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2075647286
- Cited by
- 2,665
- References
- 93
- Citations by year
- 2026: 81 · 2025: 115 · 2024: 90 · 2023: 109 · 2022: 129 · 2021: 167 · 2020: 178 · 2019: 200 · 2018: 254 · 2017: 184 · 2016: 197 · 2015: 197 · 2014: 172 · 2013: 159 · 2012: 136
- FWCI
- 4.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- brain-computer interface, EEG, pattern classification, algorithm comparison
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Communication Devices for People with Disabilities, Communication Aids for Disabled, Electroencephalography, Evoked Potentials, Pattern Recognition, Automated, User-Computer Interface
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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