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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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A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition
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“The overview, comparison, and guidelines on how to choose between EEG classificationmethods can be found in [36], [43].”
BackgroundMethodPerson authentication from neural activity of face-specific visual self-representation
Yeom, S.-K., Suk, H., & Lee, S. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.023 · Cited by 119
A survey on smartphone-based systems for opportunistic user context recognition
Hoseinitabatabaei, S. A., Gluhak, A., & Tafazolli, R. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480744 · Cited by 119
Sheikhani, A., Behnam, H., Mohammadi, M. R., Noroozıan, M., & Mohammadi, M. R. (2010) · Journal of Medical Systems · doi:10.1007/s10916-010-9560-6 · Cited by 119
Bio-signal based control in assistive robots: a survey
Rechy-Ramirez, E. J., & Hu, H. (2015) · Digital Communications and Networks · doi:10.1016/j.dcan.2015.02.004 · Cited by 118
Data-Driven-Based Analog Beam Selection for Hybrid Beamforming Under mm-Wave Channels
Long, Y., Chen, Z., Fang, J., & Tellambura, C. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2818649 · Cited by 118
“SVM is a preferred multi-class classification algorithm [21] in machine learning, which is good at handling linearly inseparable dataset of samples and avoiding over-fitting.”
BackgroundFusion of Facial Expressions and EEG for Multimodal Emotion Recognition
Huang, Y., Yang, J., Liao, P., & Pan, J. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/2107451 · Cited by 115
Deep learning for EEG data analytics: A survey
Li, G., Lee, C. H., Jung, J. J., Youn, Y. C., & Camacho, D. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5199 · Cited by 129
“Lotte et al 70 have surveyed some papers, which are about the classification algorithms used to the BCI design system-based EEG.”
MethodA prototype-based SPD matrix network for domain adaptation EEG emotion recognition
Wang, Z., Qiu, S., Ma, X., & He, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107626 · Cited by 119
A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges
Yadav, D., Yadav, S., & Veer, K. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108918 · Cited by 118
Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components
Xiao, X., Xu, M., Jin, J., Wang, Y., Jung, T.-P., & Ming, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2958641 · Cited by 113
Nonstationary Brain Source Separation for Multiclass Motor Imagery
Gouy‐Pailler, C., Congedo, M., Brunner, C., Christian Jutten, B., & Pfurtscheller, G. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2032162 · Cited by 115
Self-citationWei, C.-S., Lin, Y., Wang, Y.-T., Lin, C., & Jung, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.032 · Cited by 116
“Furthermore, applying PCA could also alleviate curse-of-dimensionality in building such predictive models (Lotte et al., 2007).”
Hazrati, M. K., & Erfanian, A. (2010) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2010.04.016 · Cited by 114
Shahabi, H., & Moghimi, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.01.005 · Cited by 116
Min, B., Kim, J., Park, H.-J., & Lee, B. (2016) · BioMed Research International · doi:10.1155/2016/2618265 · Cited by 114
A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State
Myrden, A., & Chau, T. T. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2641956 · Cited by 115
The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface
Jin, J., Li, S., Daly, I. P., Miao, Y., Liu, C., Wang, X., & Cichocki, A. S. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2956488 · Cited by 113
“Moreover, this classifier is simple to use and generally obtains better classification performance and acceptable accuracy [22], [40].”
MethodMost Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis
Wierzgała, P., Zapała, D., Wójcik, G. M., & Masiak, J. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00078 · Cited by 112
Bimodal BCI Using Simultaneously NIRS and EEG
Tomita, Y., Vialatte, F., Dreyfus, G., Mitsukura, Y., Bakardjian, H., & Cichocki, A. S. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300492 · Cited by 110
Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases
Abdar, M., Kalhori, S. R. N., Sutikno, T., Subroto, I. M. I., & Arji, G. (2015) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v5i6.pp1569-1576 · Cited by 111
Naseer, N., Qureshi, N. K., Noori, F. M., & Hong, K. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/5480760 · Cited by 111
Raza, H., Rathee, D., Zhou, S., Cecotti, H., & Prasad, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.04.087 · Cited by 111
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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