The DOI matches its Crossref record; 261 of the 266 references it cites were found in a registry or an index.
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“If the data dimension is too large, the Riemannian Geometry method will perform many calculations, which is time-consuming and causes statistical deviations [22].”
From Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier (2020) · cites this paper as [22]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“xDAWN spatial filtering [13] with Riemannian geometry (xDAWN+RG) features also perform well cross-subject [14], [15] but lack obvious physiological interpretation [8].”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“General-purpose deep learning architectures such as EEGNet [6], [7], [8] have been evaluated across multiple BCI paradigms”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Flagged first, then by citations
5 of the 266 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Allwein, 2001, Reducing multiclass to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res., 1, 113
- Chevallier, 2018, Riemannian classification for SSVEP-based BCI, 10.1201/9781351231954-19 · DOI
- Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157
Cited by
Sorted by citations
Items 301–400 of 1,003
Lee, M., Jeong, J.-H., Kim, Y., & Lee, S. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3087506 · Cited by 37
Motor imagery EEG classification algorithm based on improved lightweight feature fusion network
Yu, Z., Chen, W., & Zhang, T. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103618 · Cited by 36
Xie, Y., & Oniga, S. (2023) · Sensors · doi:10.3390/s23041932 · Cited by 40
Low dimensional subject representation based transfer learning in eeg decoding
Jeng, P.-Y., Wei, C.-S., Jung, T.-P., & Wang, L.-C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3025865 · Cited by 35
A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest
Zhang, R., Xiao, X., Liu, Z., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2860633 · Cited by 35
Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals
Dai, M., Wang, S., Zheng, D., Na, R., & Zhang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2908851 · Cited by 35
Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data
Roy, S., Rathee, D., Chowdhury, A., et al. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbd21 · Cited by 35
SSTD: A Novel Spatio-Temporal Demographic Network for EEG-Based Emotion Recognition
Li, R., Ren, C., Li, C., Zhao, N., Lu, D., & Zhang, X. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3188891 · Cited by 34
Genetic algorithm for feature selection of EEG heterogeneous data
Saibene, A., & Gasparini, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119488 · Cited by 40
EEG-based stress identification and classification using deep learning
Hafeez, M. A., & Shakil, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17111-0 · Cited by 34
“EEG signals are being used to examine different mental activities such as analyzing sleep patterns [12], detecting epilepsy [13] and stress [14], predicting mood or emotions [15], and person identification [16] and are widely used in brain-computer interface (BCI) [17].”
On the design of EEG-based movement decoders for completely paralyzed stroke patients
Spüler, M., López‐Larraz, E., & Ramos‐Murguialday, A. (2018) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-018-0438-z · Cited by 33
Moctezuma, L. A., & Molinas, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-72051-1 · Cited by 34
Bringing Back the Person into Behavioural Personality Science Using Big Data
Renner, K., Klee, S., & von Oertzen, T. (2020) · European Journal of Personality · doi:10.1002/per.2303 · Cited by 37
Multi-class Time Continuity Voting for EEG Classification
Qu, X., Liu, P., Li, Z., & Hickey, T. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60735-7_3 · Cited by 34
EEG Classification by Factoring in Sensor Spatial Configuration
Mokatren, L. S., Ansari, R., Çetin, A. E., Leow, A., Ajilore, O., Klumpp, H., & Vural, F. T. Y. (2021) · IEEE Access · doi:10.1109/access.2021.3054670 · Cited by 35
Gao, W., Yu, T., Yu, J.-G., Gu, Z., Li, K., Huang, Y., Yu, Z. L., & Li, Y. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3083548 · Cited by 36
Embedded Brain Computer Interface: State-of-the-Art in Research
Belwafi, K., Gannouni, S., & Aboalsamh, H. (2021) · Sensors · doi:10.3390/s21134293 · Cited by 34
A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding
He, Y., Lu, Z., Wang, J., Ying, S., & Shi, J. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3199363 · Cited by 36
Adapting visualizations and interfaces to the user
Chiossi, F., Zagermann, J., Karolus, J., Rodrigues, N., Balestrucci, P., Weiskopf, D., Ehinger, B., Feuchtner, T., Reiterer, H., Chuang, L. L., et al. (2022) · it - Information Technology · doi:10.1515/itit-2022-0035 · Cited by 34
Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation
Sakkalis, V., Krana, M., Farmaki, C., Bourazanis, C., Gaitatzis, D., & Pediaditis, M. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3215695 · Cited by 34
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery RecognitionPreprint
Hou, Y., Jia, S., Lun, X., Zhang, S., Chen, T., Wang, F., & Lv, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00777 · Cited by 34
Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
Bressan, G., Cisotto, G., Muller-Putz, G. R., & Wriessnegger, S. C. (2021) · RePEc: Research Papers in Economics · Cited by 34
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
Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training
Abu-Rmileh, A., Zakkay, E., Shmuelof, L., & Shriki, O. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00362 · Cited by 33
Brain‐computer interfaces for amyotrophic lateral sclerosis
McFarland, D. J. (2020) · Muscle & Nerve · doi:10.1002/mus.26828 · Cited by 38
Zhang, S., Zhu, Z., Zhang, B., Feng, B., Yu, T., & Li, Z. (2020) · Sensors · doi:10.3390/s20174749 · Cited by 34
Moctezuma, L. A., Abe, T., & Molinas, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-07517-5 · Cited by 39
Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning
Wu, X., Feng, Y., Lou, S., Zheng, H. R., Hu, B., Hong, Z., & Tan, J. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.087 · Cited by 34
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.”
MethodResultInfluentialHan, J.-W., Bak, S., Kim, J.-M., Choi, W., Shin, D.-H., Son, Y.-H., & Kam, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121986 · Cited by 36
Brain-computer interface paradigms and neural coding
Tai, P., Ding, P., Wang, F., Gong, A., Li, T., Zhao, L., Su, L., & Fu, Y. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1345961 · Cited by 35
Human-centric assembly in smart factories
Wang, L., Gao, R. X., Krüger, J., & Váncza, J. (2025) · CIRP Annals · doi:10.1016/j.cirp.2025.04.058 · Cited by 36
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
EEG-Based Motor Imagery Classification with Deep Multi-Task Learning
Song, Y., Wang, D., Yue, K., Zheng, N., & Shen, Z. M. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852362 · Cited by 31
Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE)
Corsi, M., Chevallier, S., Fallani, F. D. V., & Yger, F. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3154885 · Cited by 40
Self-citationZhang, S., Ang, K. K., Zheng, D., Hui, Q., Chen, X., Li, Y., Tang, N., Chew, E., Lim, R. Y., & Guan, C. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3209155 · Cited by 32
Xue, Z., Zhang, Y., Li, H., Chen, H., Shen, S., & Du, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3417598 · Cited by 35
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
Self-citationRenton, A., Mattingley, J. B., & Painter, D. R. (2019) · Scientific Reports · doi:10.1038/s41598-019-55166-y · Cited by 32
Hehenberger, L., Kobler, R. J., Lopes-Dias, C., Srisrisawang, N., Tumfart, P., Uroko, J. B., Torke, P. R., & Müller-Putz, G. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.635777 · Cited by 33
Cao, J., Grajcar, K., Shan, X., Zhao, Y.-F., Zou, J., Chen, L.-Y., Li, Z., Grünewald, R., Zis, P., Marco, M. D., et al. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102554 · Cited by 22
Decoding Hand Movement Types and Kinematic Information From Electroencephalogram
Xu, B., Wang, Y., Deng, L., Wu, C., Zhang, W., Li, H., & Song, A. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3106897 · Cited by 33
Deep learning-based EEG analysis: investigating P3 ERP components
Borra, D., & Magosso, E. (2021) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2004083 · Cited by 34
Klein, F., Lührs, M., Benitez-Andonegui, A., Roehn, P., & Kranczioch, C. (2022) · Neurophotonics · doi:10.1117/1.nph.10.1.013503 · Cited by 33
Gao, Y., Liu, Y., She, Q., & Zhang, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3218453 · Cited by 30
Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition
Choo, S., Park, H., Kim, S., Park, D., Jung, J., Lee, S., & Nam, C. S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120348 · Cited by 33
Canny, E., Vansteensel, M. J., van der Salm, S. M. A., Müller-Putz, G., & Berezutskaya, J. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01272-y · Cited by 38
Liao, J. J., Luo, J. J., Yang, T., So, R. Q., & Chua, M. C. H. (2020) · Brain-Computer Interfaces · doi:10.1080/2326263x.2020.1801112 · Cited by 29
Riemannian Geometry-Based Decoding of the Directional Focus of Auditory Attention Using EEG
Geirnaert, S., Francart, T., & Bertrand, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413404 · Cited by 30
Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs
Luo, J., Shi, W., Lu, N., Wang, J., Chen, H., Wang, Y., Lu, X., Wang, X., & Hei, X. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abe357 · Cited by 29
Beach, C., Li, M., Balaban, E., & Casson, A. J. (2021) · Healthcare Technology Letters · doi:10.1049/htl2.12016 · Cited by 31
Studying the generalisability of cognitive load measured with EEG
Cabañero, L., Hervás, R., Gónzalez, I., & Villarreal, V. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103032 · Cited by 35
Liu, Y., Wang, Z., Huang, S., Wang, W., & Ming, D. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac49a6 · Cited by 29
Song, Z., Deng, B., Wang, J., Yi, G., & Yue, W. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3165060 · Cited by 35
Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding
Zhi, H., Yu, T., Gu, Z., Lin, Z., Che, L., Li, Y., & Yu, Z. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3432934 · Cited by 31
Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs
Keutayeva, A., Fakhrutdinov, N., & Abibullaev, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-73755-4 · Cited by 33
The history, current state and future possibilities of the non-invasive brain computer interfaces
Caiado, F., & Ukolov, A. (2025) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2025.100353 · Cited by 33
Deep neurocomputational fusion for ASD diagnosis using multi-domain EEG analysis
Rasool, A., Aslam, S., Xu, Y., Wang, Y., Pan, Y. F., & Chen, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130353 · Cited by 30
Presurgical Functional Cortical Mapping Using Electromagnetic Source Imaging
Kreidenhuber, R., Tiège, X. D., & Rampp, S. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00628 · Cited by 28
Li, X., Qian, B., Wei, J., Li, A., Liu, X., & Zheng, Q. (2019) · Proceedings · doi:10.1109/icdm.2019.00049 · Cited by 30
Migliorelli, C., Bachiller, A., Alonso, J. F., Romero, S., Aparicio, J., Van, J. J.-L., Mañanas, M. Á., & Antonio‐Arce, V. S. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8345 · Cited by 29
Zhang, S., Zhu, Z., Zhang, B., Feng, B., Yu, T., & Li, Z. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3016402 · Cited by 29
Easy Perturbation EEG Algorithm for Spectral Importance (easyPEASI)
Nahmias, D. O., & Kontson, K. L. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403289 · Cited by 29
The performance impact of data augmentation in CSP-based motor-imagery systems for BCI applications
Gubert, P. H., Costa, M. H., Silva, C. D., & Trofino, A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102152 · Cited by 29
Review of Closed-Loop Brain–Machine Interface Systems From a Control Perspective
Pan, H., Song, H., Zhang, Q., & Mi, W. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3138677 · Cited by 30
Khaleghi, N., Rezaii, T. Y., Beheshti, S., & Meshgini, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104221 · Cited by 29
Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG
Xu, T., Wang, J., Zhang, G., Zhang, L., & Zhou, Y. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acbfe0 · Cited by 30
“Due to its good performance on EEG data and suitability for small sample size, support vector machine (SVM) is one of the most popular algorithms for building classifiers in EEG-based brain-computer interfaces [22, 59].”
Facial expression recognition based on Electroencephalogram and facial landmark localization
Li, D., Wang, Z., Gao, Q., et al. (2019) · Technology and Health Care · doi:10.3233/thc-181538 · Cited by 29
Lee, M., Park, H.-Y., Park, W., Kim, K.-T., Kim, Y., & Jeong, J.-H. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3395133 · Cited by 32
“The SDC aims to find a low-dimensional subspace in which the classes are well separated, leading to improved performance in classification tasks for dealing with high-dimensional datasets [44].”
BackgroundEvaluation of convolutional neural networks using a large multi-subject P300 dataset
Vařeka, L. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101837 · Cited by 28
Li, Z., Sun, S., Zhao, H., Pei, W., Chen, H., Gao, X., Zhang, L., & Wang, Y. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.579469 · Cited by 28
Effect of time windows in LSTM networks for EEG-based BCIs
Martín‐Chinea, K., Ortega‐Rodríguez, J., Gómez‐González, J. F., Pereda, E., Toledo, J., & Acosta, L. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09832-z · Cited by 31
Triana-Guzman, N., Orjuela-Cañón, Á. D., Jutinico, A. L., Mendoza-Montoya, O., & Antelis, J. M. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.961089 · Cited by 28
RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing
Georgiadis, K., Kalaganis, F. P., Oikonomou, V. P., Nikolopoulos, S., Laskaris, N., & Kompatsiaris, I. Y. (2022) · Brain Informatics · doi:10.1186/s40708-022-00171-7 · Cited by 32
Delisle-Rodríguez, D., de Oliveira, H. L., da Silva, J. C., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104937 · Cited by 28
A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels
Hu, L., Zhu, J., Chen, S., Zhou, Y., Song, Z., & Li, Y. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3308371 · Cited by 29
Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models
Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pise, A. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-07427-2 · Cited by 37
“The CNN model demonstrates reasonable accuracy at 0.810 but is challenged by a significant number of false positives (28) and false negatives (32).”
InfluentialGruenwald, J., Znobishchev, A., Kapeller, C., Kamada, K., Scharinger, J., & Guger, C. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00901 · Cited by 27
Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism
Penchina, B., Sundaresan, A., Cheong, S., & Martel, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_21 · Cited by 26
Zhang, W., Song, A., Zeng, H., Xu, B., & Miao, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.638638 · Cited by 27
Xi, X., Sun, Z., Hua, X., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.102 · Cited by 27
Huang, J., Yang, P., Xiong, B., Wan, B., Su, K., & Zhang, Z. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3162029 · Cited by 27
Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis
Dehais, F., Ladouce, S., Darmet, L., Nong, T.-V., Ferraro, G., Torre, J. J., Velut, S., & Labedan, P. (2022) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.824780 · Cited by 27
Delisle-Rodríguez, D., Silva, L., & Bastos-Filho, T. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acb73b · Cited by 27
Ge, X., Pan, Y., Wang, S., Qian, L., Yuan, J., Xu, J., Thakor, N. V., & Sun, Y. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3225633 · Cited by 26
Li, H., Tang, J., Li, W., Dai, W., Liu, Y., & Zhou, Z. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3357863 · Cited by 26
“Over the past decades, conventional and deep learning classification methods have been used to process RSVP EEG signals, and Lotte et al. [11], [12] have summarized these methods.”
MethodGe, S., Jiang, Y., Wang, P., Wang, H., & Zheng, W. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2934496 · Cited by 26
Yılmaz, B. H., Yılmaz, Ç. M., & Köse, C. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02075-x · Cited by 26
Review on EEG-BCI classification techniques advancements
Janapati, R., Dalal, V., Govardhan, N., et al. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/981/3/032019 · Cited by 26
Sosnik, R., & Li, Z. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abf0d7 · Cited by 26
MCGNet+: an improved motor imagery classification based on cosine similarity
Li, Y., Zhong, N., Taniar, D., & Zhang, H. (2022) · Brain Informatics · doi:10.1186/s40708-021-00151-3 · Cited by 26
An improved self-supervised learning for EEG classification
Ou, Y., Sun, S., Gan, H., Zhou, R., & Yang, Z. Q. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022325 · Cited by 26
Leoni, J., Strada, S. C., Tanelli, M., Brusa, A., & Proverbio, A. M. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100393 · Cited by 27
Sosulski, J., & Tangermann, M. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac9c98 · Cited by 26
Connectivity steered graph Fourier transform for motor imagery BCI decoding
Georgiadis, K., Laskaris, N., Nikolopoulos, S., & Kompatsiaris, I. Y. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab21fd · Cited by 26
Boshra, R., Dhindsa, K., Boursalie, O., Ruiter, K. I., Sonnadara, R., Samavi, R., Doyle, T. E., Reilly, J. P., & Connolly, J. F. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2922553 · Cited by 29
Zhang, K., Xu, G., Chen, L., Tian, P., Han, C., Zhang, S., & Duan, N. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/1683013 · Cited by 25
Dreyer, P., Roc, A., Pillette, L., Rimbert, S., & Lotte, F. (2023) · Scientific Data · doi:10.1038/s41597-023-02445-z · Cited by 26
“This yielded to 6 different features per epoch, which were used as input to a Linear Discriminant Analysis (LDA) classifier 6 .”
MethodSelf-citationA Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding
Ouahidi, Y. E., Gripon, V., Pasdeloup, B., Bouallegue, G., Farrugia, N., & Lioi, G. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3451010 · Cited by 25
“Nevertheless, the utilization of Deep Learning in BCIs has not yet demonstrated convincing performance and robustness, and the literature continues to encounter challenges [4].”
Background
Metadata
- 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
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
