Cited by 2,665EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyEmotion and Mood Recognition

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A review of classification algorithms for EEG-based brain–computer interfaces

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Lotte, F, Congedo, M, Lécuyer, A, Lamarche, F, Arnaldi, B·2007·Journal of Neural Engineering

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Abstract

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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“There are different ways to combine classifiers to perform classification: Bagging, Boosting, Voting and Stacking [16, 17, 51].”
BackgroundMethod
Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance
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“Fig 3 illustrates the training and validation performance with 500 epochs, where the hyper-parameters are: Frequency: [8- 30] Hz, dropout: 0.25, Kernel Length = 64.”
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BackgroundMethodInfluential
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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)
ORCID: Lotte, F
Congedo, M
Lécuyer, A
Lamarche, F
Arnaldi, B
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
DOI: 10.1088/1741-2560/4/2/r01
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

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A review of classification algorithms for EEG-based brain–computer interfaces

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