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 BCI paradigm relies on manual specification of signal processing, feature extraction and classification methods, requiring significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.
“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]
Section 2.2 says that choosing an appropriate classifier requires knowing the properties of available classifiers, and Section 4 provides guidelines for choosing one for a given BCI context. This supports the classification-method selection part attributed to [9], but the paper does not establish that every paradigm requires manual specification or that the process requires significant subject-matter expertise or prior knowledge of the expected EEG signal. Page numbers are unavailable in our copy. (LLM, conf=0.81)
Experimental — accuracy not yet measuredSVM 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.
“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]
The reliance on human expertise is a necessary problem to be solved in BCI processes.
“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]
The Introduction describes automatic classification in EEG-BCI systems, and §2.2.2 identifies dimensionality and the bias–variance tradeoff as classification problems. Neither passage says that BCI processes rely on human expertise or that this reliance must be solved. Searches for expert, human intervention, and manual design found no passage addressing that point; page numbers were unavailable in our copy. (LLM, conf=0.70)
Experimental — accuracy not yet measured
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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)
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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