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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Background
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Background
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Method
EEG Classification by Factoring in Sensor Spatial Configuration
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Method
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Background
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“We used a support vector machine classifier (SVM) due to its excellent performance in EEG classification in previous studies of mind-wandering (Jin et al., 2019; Lotte, Congedo, Lecuyer, Lamarche, & Arnaldi, 2007).”
Method
Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling
Klaproth, O. W., Vernaleken, C., Krol, L. R., Halbrügge, M., Zander, T. O., & Rußwinkel, N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00795 · Cited by 33
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“It is very sensitive to the curseof-dimensionality, especially with a typical 64 or 128 electrodes EEG headset; however, it works well with low-dimennsional feature vectors [16, 17].”
BackgroundMethod
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Method
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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].”
Background
A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration
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A Multi-Class BCI Based on Somatosensory Imagery
Yao, L., Mrachacz‐Kersting, N., Sheng, X., Zhu, X., Farina, D., & Jiang, N. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2848883 · Cited by 33
Decoding Neural Correlates of Cognitive States to Enhance Driving Experience
Chavarriaga, R., Ušćumlić, M., Zhang, H., Khaliliardali, Z., Aydarkhanov, R., Saeedi, S., Gheorghe, L., & Millán, J. D. R. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2848289 · Cited by 33
“Nonetheless, simple techniques have low computational complexity that is critical for real-time applications while preserving a satisfactory single-trial decoding performance [27], [28].”
Background
Spatio-temporal discrepancy feature for classification of motor imageries
Luo, J., Feng, Z., & Lu, N. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.07.003 · Cited by 33
The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application
Rashid, M., Sulaiman, N., Mustafa, M., Khatun, S., & Bari, B. S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-7780-8_17 · Cited by 33
Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy
Lees, S., McCullagh, P. J., Payne, P., Maguire, L., Lotte, F., & Coyle, D. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2953975 · Cited by 33
“A new LDA [31] classifier (using 50% of data) was produced to classify target vs nontarget data on remaining 50% of unseen testing data.”
BackgroundMethodSelf-citation
A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping
Lampe, T., Fiederer, L. D. J., Voelker, M., Knorr, A. G., Riedmiller, M., & Ball, T. (2014) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2557500.2557533 · Cited by 34
EEG Responses to Auditory Stimuli for Automatic Affect Recognition
Hettich, D. T., Bolinger, E., Matuz, T., Birbaumer, N., Rosenstiel, W., & Spüler, M. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00244 · Cited by 34
Fuzzy Feature Extraction for Multichannel EEG Classification
Zhou, P.-Y., & Chan, K. C. C. (2016) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2016.2632130 · Cited by 33
“The tests that we performed include the following: 1) classify each selected channel of EEG data independently to determine average classification accuracy (original data); 2) raw-based classification that preprocess the data with CAR and then classify each channel of EEG data independently to determine average classi…”
Method
Harnessing Prefrontal Cognitive Signals for Brain–Machine Interfaces
Min, B., Chavarriaga, R., & Millán, J. D. R. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.03.008 · Cited by 35
Long-term kinesthetic motor imagery practice with a BCI: Impacts on user experience, motor cortex oscillations and BCI performances
Rimbert, S., & Fleck, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107789 · Cited by 32
“Considering the literature on ERD/ERS [47, 82] and the classification performance used in BCI [60, 58], 80 trials of each class is relevant to estimate the accuracy.”
Method
Local and global convolutional transformer-based motor imagery EEG classification
Zhang, J., Li, K., Yang, B., & Han, X. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1219988 · Cited by 39
“As an end-to-end signal processing method, Deep Learning (DL) has been successfully applied in extracting and analyzing abstract information from MI-EEG signals in recent years (Lotte et al., 2007; Zancanaro et al., 2023).”
Method
Support matrix machine: A review
Kumari, A. R., Akhtar, M., Shah, R., & Tanveer, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106767 · Cited by 38
Hybrid EEG-NIRS based BCI for quadcopter control
Khan, M. J., Hong, K., Naseer, N., & Bhutta, M. R. (2015) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2015.7285434 · Cited by 31
A novel Morse code-inspired method for multiclass motor imagery brain–computer interface (BCI) design
Jiang, J., Zhou, Z., Yin, E., Yu, Y., Liu, Y., & Hu, D. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.08.011 · Cited by 32
Simple adaptive sparse representation based classification schemes for EEG based brain–computer interface applications
Shin, Y., Lee, S., Ahn, M., Cho, H., Jun, S. C., & Lee, H.-N. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.08.017 · Cited by 32
Applying evolution strategies to preprocessing EEG signals for brain–computer interfaces
Aler, R., Galván, I. M., & Valls, J. M. (2012) · Information Sciences · doi:10.1016/j.ins.2012.05.012 · Cited by 31
Mind reading with regularized multinomial logistic regression
Huttunen, H. J., Manninen, T., Kauppi, J., & Tohka, J. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0464-y · Cited by 33
New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot
Çinar, E. M., & Sahin, F. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0744-x · Cited by 32
On classifiability of wavelet features for EEG-based brain-computer interfaces
Sherwood, J., & Derakhshani, R. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178939 · Cited by 33
A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near‐infrared spectroscopy
Karamzadeh, N. S., Amyot, F., Kenney, K., Anderson, A. A., Chowdhry, F. A., Dashtestani, H., Wassermann, E. M., Chernomordik, V., Boccara, C., Wegman, E. J., et al. (2016) · Brain and Behavior · doi:10.1002/brb3.541 · Cited by 33
Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain–Computer Interfaces
Abibullaev, B., & Zollanvari, A. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2883458 · Cited by 32
“, the number of channels × the number of time points [28], [29]).”
Background
Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing
Wittevrongel, B., & Hulle, M. M. V. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00630 · Cited by 32
A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition.
Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00246 · Cited by 29
Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals
Gour, N., Hassan, T., Owais, M., Ganapathi, I. I., Khanna, P., Seghier, M. L., & Werghi, N. (2023) · Brain Informatics · doi:10.1186/s40708-023-00201-y · Cited by 32
“1 Introduction Electroencephalography (EEG) signals are widely used in many applications related to brain–computer interfacing [1, 2], motor imagery classification [3–5], emotion recognition [6, 7], neuroscience [8, 9], and biomedical engineering [10, 11].”
Background
A survey of the dummy face and human face stimuli used in BCI paradigm
Chen, L., Jin, J., Zhang, Y., Wang, X., & Cichocki, A. S. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.10.002 · Cited by 31
Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification
Lu, N., Li, T., Pan, J., Ren, X., Feng, Z., & Miao, H. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.02.010 · Cited by 31
Motor Imagery Data Classification for BCI Application Using Wavelet Packet Feature Extraction
Hettiarachchi, I. T., Nguyen, T. T., & Nahavandi, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12643-2_63 · Cited by 31
Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels
Yang, Y., Chevallier, S., Wiart, J., & Isabelle Bloch, A. (2014) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/1687-6180-2014-38 · Cited by 31
Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k -NN Classifier
Hasan, M. K., Ahamed, M. A., Ahmad, M., & Rashid, M. A. (2017) · Applied Bionics and Biomechanics · doi:10.1155/2017/6848014 · Cited by 31
Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
Martı́n-Clemente, R., Olias, J., Thiyam, D. B., Cichocki, A. S., & Cruces, S. (2018) · Entropy · doi:10.3390/e20010007 · Cited by 31
Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]
Hübner, D., Verhoeven, T., Müller, K., Kindermans, P.-J., & Tangermann, M. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2807039 · Cited by 31
“Importantly, visual ERP-based BCIs often have the advantage that the stimulus presentation mode leads to a special structure of the collected brain signal data, which can be exploited by unsupervised learning methods.”
MethodInfluential
Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex
Peng, H., Chao, J., Wang, S., Dang, J., Jiang, F., Hu, B., & Majoe, D. (2018) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2018.2839736 · Cited by 32
“It is very suitable for high dimensional signals which its error-tolerant rate can be regulated owing to the choice of a regularization parameter [36].”
Background
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder
Simões, M. A., Monteiro, R., Andrade, J., Mouga, S., França, F. M. G., Oliveira, G., Carvalho, P., & Castelo-Branco, M. S. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00791 · Cited by 31
Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification
Luo, J., Wang, J., Xu, R., & Xu, K. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.05.011 · Cited by 31

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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