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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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“vector machine are the most classical approaches [7]–[11].”
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Complex networks and deep learning for EEG signal analysis
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“Brain–computer interface (BCI) (Aggarwal and Chugh 2019; Lotte et al. 2007) utilizes the physiological signals from brain to control the external devices, where the physiological signals are intentionally induced by the specific activities of the subject.”
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Brain-computer interface technologies: from signal to action
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Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI
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STEW: Simultaneous Task EEG Workload Data Set
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BackgroundInfluentialGMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
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“Traditional machine learning-based methods typically adopt the statistical measure, discrete wavelet transform, or power spectrum density [8] as features and then classify the extracted features using SVM, LDA, or LR [22].”
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Classification of electroencephalogram signals with combined time and frequency features
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Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard
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Visual modifications on the P300 speller BCI paradigm
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Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach
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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
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