Cited by 2,239EEG and Brain-Computer InterfacesEmotion and Mood RecognitionGaze Tracking and Assistive Technology

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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

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Lotte, F, Bougrain, L, Cichocki, A, Clerc, M, Congedo, M, Rakotomamonjy, A, Yger, F·2018·Journal of Neural Engineering

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Abstract

Objective . Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach . We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results . We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance . This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

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“…features are subsequently given as input to classical machine learning classifiers (e.g., support vector machines (SVMs), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN), etc.) for seizures detection, sleep staging, motor imagery classifications and etc. (Lotte et al., 2018).”
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“B RAIN-COMPUTER interfaces (BCIs) provide direct interaction between the human brain and external devices by decoding neural signals into control commands [1], [2], [3], [4], [5].”
Background
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“In recent years, there are more and more researchers applying machine learning techniques to EEG analysis (Lotte et al., 2018), including various applications such as fatigue detection in drivers (Zeng et al.”
Background
Knowledge distillation based lightweight domain adversarial neural network for electroencephalogram-based emotion recognition
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“Conventional methods for augmenting signal data often involve simplistic approaches like adding Gaussian noise, applying time-shifting, or altering the amplitude, either obscuring crucial features of the signal or failing to introduce meaningful diversity [23].”
Recognizing drivers’ turning intentions with EEG and eye movement
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EEG in Education: A Scoping Review of Hardware, Software, and Methodological Aspects
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“The study of the characteristics of brain waves and their distribution on the scalp has created a link between them and specific states of The degree of correlation or synchronization among signals from di ﬀ erent electrodes can be a measurement of connectivity pa tt erns [15]. mind.”

Authors: Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F.
Affiliations: Centre National de la Recherche Scientifique (FR)
RIKEN Center for Brain Science (JP)
Skolkovo Institute of Science and Technology (RU)
Université Côte d'Azur (FR)
GIPSA-Lab (FR)
Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (FR)
Université Paris Sciences et Lettres (FR)
Corresponding author: Lotte, F
ORCID: Lotte, F
Bougrain, L
Cichocki, A
Clerc, M
Congedo, M
Rakotomamonjy, A
Yger, F
Venue: Journal of Neural Engineering
Publisher: IOP Publishing
Published: 2018-04-16
Volume: 15
Issue: 3
Pages: 031005
ISSN: 1741-2552 (ISSN-L)
1741-2560
DOI: 10.1088/1741-2552/aab2f2
OpenAlex: W2794345050
Cited by: 2,239
References: 266
Citations by year: 2026: 320 · 2025: 284 · 2024: 262 · 2023: 283 · 2022: 276 · 2021: 321 · 2020: 273 · 2019: 170 · 2018: 49
FWCI: 27.23
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Journal of Neural Engineering (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion, other-oa)
Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine) (submittedVersion, cc-by-sa)
Funders: Polish National Science Center (2016/20/W/N24/00354)
Ministry of Education and Science of the Russian Federation (14.756.31.0001)
Agence Nationale de la Recherche (ANR-15-CE23-0013-01)
Japan Society for the Promotion of Science
H2020 European Research Council (ERC-2016-STG-714567)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords: EEG-based brain-computer interface, EEG classification, machine learning classifiers, adaptive classifier, unsupervised adaptation, transfer learning, deep learning, Riemannian geometry, random forest, small training samples
MeSH terms: Deep Learning, Algorithms, Animals, Brain, Electroencephalography, Humans, Signal Processing, Computer-Assisted, Time Factors, Brain-Computer Interfaces

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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

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