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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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In the citing paper
“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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Solving Challenges at the Interface of Simulation and Big Data Using Machine Learning
Giabbanelli, P. J. (2019) · Winter Simulation Conference · doi:10.1109/wsc40007.2019.9004755 · Cited by 17
“within the context of ElectroEncephaloGraphy (Lotte et al. 2007).”
MethodImproving motor imagery classification during induced motor perturbations
Vidaurre, C., Jorajuría, T., Ramos‐Murguialday, A., Müller, K.-R., Gómez, M., & Nikulin, V. V. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac123f · Cited by 17
Significant of Gradient Boosting Algorithm in Data Management System
Hosen, M. S., & Amin, R. (2021) · Engineering International · doi:10.18034/ei.v9i2.559 · Cited by 17
“The same boosted outfit positioning approach can be embraced in issues with neural movement information (Lotte et al., 2007).”
BackgroundEEG emotion Enhancement using Task-specific Domain Adversarial Neural Network
Ding, K.-M., Kimura, T., Fukui, K., & Numao, M. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533310 · Cited by 17
“Machine learning techniques have been applied to classify certain emotions from EEG signals [4].”
Mirjalili, S., Powell, P. S., Strunk, J., James, T., & Duarte, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118851 · Cited by 17
“Moreover, the machine learning literature strongly supports feature selection for improving classification performance and avoiding overfitting (Dias, Jacinto, Mendes, & Correia, 2009; Koprinska, 2009; Lotte et al., 2017).”
BackgroundMethodAbibullaev, B., Kunanbayev, K., & Zollanvari, A. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3189576 · Cited by 18
“classification [34], system design [35], and clinical applications [36].”
BackgroundBrain-Computer Interface for Stroke Rehabilitation
Mane, R., Ang, K. K., & Guan, C. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_33 · Cited by 16
A Comparative Review of Detection Methods in SSVEP-Based Brain-Computer Interfaces
Besharat, A., Samadzadehaghdam, N., & Afghan, R. (2024) · IEEE Access · doi:10.1109/access.2024.3509275 · Cited by 18
Online classification of two mental tasks using a SVM-based BCI system
Hortal, E., Úbeda, A., Iáñez, E., Planelles, D., & Azorín, J. M. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696181 · Cited by 17
An embedded implementation of home devices control system based on brain computer interface
Belwafi, K., Ghaffari, F., Romain, O., & Djemal, R. (2014) · doi:10.1109/icm.2014.7071826 · Cited by 17
Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method
Batres-Mendoza, P., Ibarra-Manzano, M.-A., Guerra-Hernandez, E. I., Almanza-Ojeda, D.-L., Montoro-Sanjose, C. R., Romero-Troncoso, R. D. J., & Rostro‐González, H. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/9817305 · Cited by 17
Information Theoretic Feature Transformation Learning for Brain Interfaces
Özdenizci, O., & Deniz Erdogmus, B. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2908099 · Cited by 17
“Likewise, a vast body of contemporary brain interfaces rely on subject-specific feature selection methods for feature dimensionality reduction [6]–[10], particularly based on maximum mutual information criterion [11]–[13], which were also investigated by two extensive, recent and complementary survey studies on BCIs […”
BackgroundSpinnato, J., Roubaud, M. C., Burle, B., & Torrésani, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/036013 · Cited by 17
“You Have Reached Your Destination”: A Single Trial EEG Classification Study
Wirth, C., Toth, J., & Arvaneh, M. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00066 · Cited by 17
“In this study, classifying EEG responses to correct movements toward the target (but not reaching it) against responses to movements that reached the target, we achieved mean overall classification accuracy of 66.5 and 68.0% for the two tasks.”
BackgroundInfluentialMaghsoudi, A. (2022) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.1264 · Cited by 17
“In the last decade, a wide variety of machine learning, including feature selection and classification methods have been used to EEG for developing MI-BCIs, such as, principal component analysis [23, 24], independent component analysis [25], sequential floating forward search [26], an evolutionary algorithm [27] and r…”
MethodAn improved version of local activities estimation to enhance motor imagery classification
Togha, M. M., Salehi, M. R., & Abiri, E. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102485 · Cited by 17
A review on brain imaging techniques for BCI applications
Bhattacharyya, S., Khasnobish, A., Ghosh, P., Mazumder, A., & Tibarewala, D. N. (2017) · Medical Imaging · doi:10.4018/978-1-5225-0571-6.ch012 · Cited by 8
A Plug&Play P300 BCI Using Information Geometry
Barachant, A., & Congedo, M. (2014) · arXiv · doi:10.48550/arxiv.1409.0107 · Cited by 102
Self-citation
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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