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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Method
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Background
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MethodResult
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Background
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Anitha, T., Shanthi, N., Sathiyasheelan, R., Emayavaramban, G., & Rajendran, T. (2019) · The Open Biomedical Engineering Journal · doi:10.2174/1874120701913010127 · Cited by 47
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Ma, Y., Gong, A., Nan, W., Ding, P., Wang, F., & Fu, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010046 · Cited by 52
“Designing a personalized BCI system includes a personalized BCI paradigm [15,18] for a specific BCI user, a specific brain signal acquisition scheme suitable for the user, and a personalized brain signal processing algorithm [19,31] (including a specific preprocessing model, personalized channel selection, personalize…”
BackgroundMethodInfluential
Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces
Yang, Y., Isabelle Bloch, A., Chevallier, S., & Wiart, J. (2016) · Cognitive Computation · doi:10.1007/s12559-015-9379-z · Cited by 48
Recent Advances in Resting-State Electroencephalography Biomarkers for Autism Spectrum Disorder—A Review of Methodological and Clinical Challenges
Heunis, T.-M., Aldrich, C., & de Vries, P. J. (2016) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2016.03.010 · Cited by 48
A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation
Yao, L., Sheng, X., Zhang, D., Jiang, N., Mrachacz‐Kersting, N., Zhu, X., & Farina, D. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2684084 · Cited by 48
A conceptual space for EEG-based brain-computer interfaces
Kosmyna, N., & Lécuyer, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210145 · Cited by 50
“Although some domains concerned by BCIs such as signal processing, propose the state of the art papers about the classification algorithms like [99, 100], no works exist to the general classification of BCI systems.”
BackgroundSelf-citation
Mental State Recognition via Wearable EEGPreprint
Bashivan, P., Rish, I., & Heisig, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.00985 · Cited by 48
Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network
Liu, D., Zhang, J., Wu, H., Liu, S., & Long, J. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3219418 · Cited by 50
“First, different subjects have different neural responses to the same stimulus, and even the same subject can have different neural responses to the same stimulus at different time/locations [7].”
Background
Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations
Park, S., Hwang, H., Lim, J., Choi, J., Jung, H.-K., & Im, C. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-1026-1 · Cited by 48
Predicting Targets of Human Reaching Motions Using Different Sensing Technologies
Novak, D., Omlin, X., Leins-Hess, R., & Riener, R. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2262455 · Cited by 47
A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery
Elstob, D., & Secco, E. L. (2016) · International Journal of Advanced Information Technology · doi:10.5121/ijitcs.2016.6103 · Cited by 48
Brain computer interface: A review
Prashant, P., Joshi, A. Y., & Gandhi, V. (2015) · Nirma University International Conference on Engineering · doi:10.1109/nuicone.2015.7449615 · Cited by 48
EEG-Based Detection of Starting and Stopping During Gait Cycle
Hortal, E., Úbeda, A., Iáñez, E., Azorín, J. M., & Fernández, E. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500295 · Cited by 47
Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection
Liu, Y., Huang, S., & Huang, Y.-D. (2017) · Sensors · doi:10.3390/s17071557 · Cited by 47
Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly
Kosch, T., Funk, M., Schmidt, A., & Chuang, L. L. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3229093 · Cited by 46
Using EEG to Distinguish Between Writing and Typing for the Same Cognitive Task
Qu, X., Mei, Q., Liu, P., & Hickey, T. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60735-7_7 · Cited by 45
“Nearest Neighbour: k-Nearest Neighbour(kNN) [13, 14].”
Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review
Salahuddin, U., & Gao, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.728178 · Cited by 50
Brain–Computer Interface Integrated With Augmented Reality for Human–Robot Interaction
Fang, B., Ding, W., Sun, F., Shan, J., Wang, X., Wang, C., & Zhang, X. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3194603 · Cited by 47
Transfer learning promotes acquisition of individual BCI skills
Kumar, S., Alawieh, H., Rácz, F. S., Fakhreddine, R., & Millán, J. D. R. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae076 · Cited by 51
“Having demonstrated transfer learning with Riemannian MDM decoders, we also investigated the adaptability of the GR framework to common spatial pattern-based (CSP) classifiers, which is widely used in MI – BCIs (35, 36).”
Method
Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition
Kasabov, N. K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30687-7_12 · Cited by 47
Human scalp EEG processing: Various soft computing approaches
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A comparison of classification techniques for a gaze-independent P300-based brain–computer interface
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EEG in classroom: EMD features to detect situational interest of students during learning
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Feature subset and time segment selection for the classification of EEG data based motor imagery
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Affective signal processing (ASP) : unraveling the mystery of emotions
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Single-Trial Classification of Different Movements on One Arm Based on ERD/ERS and Corticomuscular Coherence
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“• Classification: We applied linear discriminant analysis (LDA) [40], multi-layer perceptron (MLP) [41], Gaussian support vector machine (Gaussian SVM) [40], and sparse Bayesian extreme learning machine (SBELM) [42] which were commonly used in previous studies for EEG classification.”
BackgroundMethodInfluential
Early classification of motor tasks using dynamic functional connectivity graphs from EEG
Shamsi, F., Haddad, A., & Najafizadeh, L. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abce70 · Cited by 45
“Among different types of BCIs, electroencephalography (EEG)-based BCIs have received considerable attention in the BCI research community due to their advantages including non-invasiveness, high temporal resolution, low-cost, and portability [1, 2].”
A Cross-Space CNN With Customized Characteristics for Motor Imagery EEG Classification
Hu, Y., Liu, Y., Zhang, S., Zhang, T., Dai, B., Peng, B., Yang, H., & Dai, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3249831 · Cited by 45
“In recent years, methods for signal classification by extracting the time, frequency, and space domain characteristics of multi-This channel MI-EEG have been developed to a certain extent [12].”
Method

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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