Cited by 2,665EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyEmotion and Mood Recognition

The DOI matches its Crossref record; 91 of the 93 references it cites were found in a registry or an index.

A review of classification algorithms for EEG-based brain–computer interfaces

Open access

Lotte, F, Congedo, M, Lécuyer, A, Lamarche, F, Arnaldi, B·2007·Journal of Neural Engineering

Open this paper in CoChat — free account Open PDF

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.

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

Crossref · 10.1088/1741-2560/4/2/r01

Cites

91 of 91 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,616
The self-organizing map
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Stacked generalization
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Statistical pattern recognition: a review
Jain, A. K., Duin, P., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,898
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,420
Electroencephalography—Basic principles, clinical applications and related fields
(1982) · Journal of the Autonomic Nervous System · doi:10.1016/0165-1838(82)90067-4 · Cited by 3,449

Show all 93 references

Research works printed but not linked: 25

Duda R O, 2001, Pattern Recognition
Rakotomamonjy A Guigue V Mallet G Alvarado V, 2005, Int. Conf. on Artificial Neural Networks
Penny W D Roberts S J, 1999, Proc. Int. Joint Conf. on Neural Networks
Ng A Jordan M, 2002, Proc. Advances in Neural Information Processing
Rubinstein Y D Hastie T, 1997, Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining
Jain A K, 1982, Handbook of Statistics, 2
Blankertz B, 2002, Adv. Neural Inf. Process. Syst. (NIPS 01), 14, 157
Anderson C W Sijercic Z, 1996, Solving Engineering Problems with Neural Networks: Proc. Int. Conf. on Engineering Applications of Neural Networks (EANN'96)
Palaniappan R, 2005, Proc. 2nd Int. IEEE EMBS Conf. on Neural Engineering
Balakrishnan D Puthusserypady S, 2005, Proc. IEEE 31st Annual Northeast Bioengineering Conference
Millán J R Mouriño J Cincotti F Babiloni F Varsta M Heikkonen J, 2000, IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks
Solhjoo S Moradi M H, 2004, BIOSIGNAL 2004 Int. EURASIP Conference
Cincotti F Scipione A Tiniperi A Mattia D Marciani M G Millán J del R Salinari S Bianchi L Babiloni F, 2003, Proc. 1st Int. IEEE EMBS Conf. on Neural Engineering
Barreto A B Taberner A M Vicente L M, 1996, Southeastcon '96. ‘Bringing Together Education, Science and Technology’, Proc. IEEE
Hoya T Hori G Bakardjian H Nishimura T Suzuki T Miyawaki Y Funase A Cao J, 2003, Proc. ICA2003, 197
Tavakolian K Rezaei S, 2004, Proc. IEEE Int. Workshop on Biomedical Circuits and Systems
Barreto G A Frota R A de Medeiros F N S, 2004, Proc. IEEE Workshop on Machine Learning for Signal Processing
Solhjoo S Nasrabadi A M Golpayegani M R H, 2005, Proc. European Signal Processing Conference
Hoffmann U Garcia G Vesin J-M Diserens K Ebrahimi T, 2005, Conference Proc. 2nd Int. IEEE EMBS Conf. Neural Engineering
Lee H Choi S, 2003, Proc. 7th Int. Symp. on Signal Processing and its Applications
Lotte F, 2006, Proc. 3rd Int. Brain-Computer Interface Workshop and Training Course, 12
Xu W Guan C Siong C E Ranganatha S Thulasidas M Wu J, 2004, 17th Int. Conf. on Pattern Recognition (ICPR'04), 2, 391
Tavakolian K Vasefi F Naziripour K Rezaei S, 2006, Proc. Canadian Student Conf. on Biomedical Computing
Arrouët C, 2004, J. Neurother.
Garcia G Ebrahimi T Vesin J-M, 2002, 14th Int. Conf. on Digital Signal Processing (DSP2002)

Sorted by citations

Items 301–400 of 1,001

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states
Sabbagh, D., Ablin, P., Varoquaux, G. P., Gramfort, A., & Engemann, D. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116893 · Cited by 85
Subject-based feature extraction by using fisher WPD-CSP in brain–computer interfaces
Yang, B., Li, H., Wang, Q., & Zhang, Y. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.02.020 · Cited by 80
Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions
Lalitharatne, T. D., Teramoto, K., Hayashi, Y., & Kiguchi, K. (2013) · Paladyn Journal of Behavioral Robotics · doi:10.2478/pjbr-2013-0009 · Cited by 79
Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance
Naros, G., Naros, I., Grimm, F., Ziemann, U., & Gharabaghi, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.016 · Cited by 85
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI
Sundaresan, A., Penchina, B., Cheong, S., Grace, V., Valero‐Cabré, A., & Martel, A. (2021) · Brain Informatics · doi:10.1186/s40708-021-00133-5 · Cited by 84
A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses
Cecotti, H. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.02.022 · Cited by 80
Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms
Abibullaev, B., & An, J. (2012) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2012.01.002 · Cited by 78
Comparison of designs towards a subject-independent brain-computer interface based on motor imagery
Lotte, F., Guan, C., & Ang, K. K. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334126 · Cited by 78
Self-citation
Multistep Prediction of Physiological Tremor Based on Machine Learning for Robotics Assisted Microsurgery
Tatinati, S., Veluvolu, K. C., & Ang, W. T. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2381495 · Cited by 77
Improving time–frequency domain sleep EEG classification via singular spectrum analysis
Mohammadi, S. M., Kouchaki, S., Ghavami, M., & Sanei, S. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.08.008 · Cited by 77
Deep Fusion Feature Learning Network for MI-EEG Classification
Yang, J. L., Yao, S., & Wang, J. (2018) · IEEE Access · doi:10.1109/access.2018.2877452 · Cited by 77
Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules
Chapotot, F., & Becq, G. (2009) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.1147 · Cited by 77
Classification of primitive shapes using brain–computer interfaces
Esfahani, E. T., & Sundararajan, V. (2011) · Computer-Aided Design · doi:10.1016/j.cad.2011.04.008 · Cited by 77
Brain communication in the locked-in state
Massari, D. D., Ruf, C. A., Furdea, A., Matuz, T., van der Heiden, L., Halder, S., Silvoni, S., & Birbaumer, N. (2013) · Brain · doi:10.1093/brain/awt102 · Cited by 79
EEG-Based Detection of Braking Intention Under Different Car Driving Conditions
Hernández, L., Mozos, Ó. M., Vicente, J. M. F., & Antelis, J. M. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00029 · Cited by 77
“In addition, we used a Support Vector Machine (SVM) for the classification since this algorithm has showed good performance in applications with EEG signals (Lotte et al., 2007; Vega et al., 2017).”
Method
A SINGLE-SWITCH BCI BASED ON PASSIVE AND IMAGINED MOVEMENTS: TOWARD RESTORING COMMUNICATION IN MINIMALLY CONSCIOUS PATIENTS
Müller-Putz, G., Pokorny, C., Klobassa, D. S., & Horki, P. (2012) · International Journal of Neural Systems · doi:10.1142/s0129065712500372 · Cited by 75
Decoding Upper Limb Movement Attempt From EEG Measurements of the Contralesional Motor Cortex in Chronic Stroke Patients
Antelis, J. M., Montesano, L., Ramos‐Murguialday, A., Birbaumer, N., & Mínguez, J. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2541084 · Cited by 76
Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation
Bhagat, N. A., Yozbatıran, N., Sullivan, J. L., Paranjape, R. P., Losey, C., Hernandez, Z., Keser, Z., Grossman, R. G., Francisco, G. E., O’Malley, M. K., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102502 · Cited by 82
“To detect motor intent we identified movement related cortical potentials from delta-band EEG rhythms (0.1 – 1 Hz), using a Go vs. No-go Support Vector Machine (SVM) classifier (Lotte et al., 2007).”
Influential
Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification
Wen, T., & Zhang, Z. (2017) · Medicine · doi:10.1097/md.0000000000006879 · Cited by 75
Spatiotemporal Representations of Rapid Visual Target Detection: A Single-Trial EEG Classification Algorithm
Fuhrmann Alpert, G., Manor, R., Spanier, A. B., Deouell, L. Y., & Geva, A. B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2289898 · Cited by 74
EEG- and EOG-Based Asynchronous Hybrid BCI: A System Integrating a Speller, a Web Browser, an E-Mail Client, and a File Explorer
He, S., Zhou, Y., Yu, T., Zhang, R., Huang, Q., Chuai, L., Mustafa, M.-U., Gu, Z., Yu, Z. L., Tan, H., et al. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2961309 · Cited by 75
“, P300 and SSVEPs) usually encounter a problem of low ITR or high FPR in the control or idle state because of the low SNRs of EEG [25].”
Background
Exploiting Transfer Learning for Emotion Recognition Under Cloud-Edge-Client Collaborations
Wu, D., Han, X., Yang, Z., & Wang, R. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020677 · Cited by 74
“SVM, nonlinear Bayesian networks, K-nearest neighbor are commonly adopted classifiers [12], [29].”
Method
Progress in EEG-Based Brain Robot Interaction Systems
Mao, X., Li, M., Li, W., Niu, L., Xian, B., Zeng, M., & Chen, G. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/1742862 · Cited by 74
Defining and quantifying users’ mental imagery-based BCI skills: a first step
Lotte, F., & Jeunet, C. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aac577 · Cited by 74
“The resulting 12 band power features were classiﬁed using a multi-class LDA, built by combining three LDA in a one-versus-the-rest scheme, as in [20].”
MethodInfluentialSelf-citation
Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System
Nguyen, T. T., Hettiarachchi, I. T., Khatami, A., Gordon-Brown, L., Lim, C. P., & Nahavandi, S. (2018) · IEEE Access · doi:10.1109/access.2018.2841051 · Cited by 76
“FLS demonstrates a significant dominance over LDA, KNN and ensemble AdaBoostM2, at the significance level of α = 0.05, and over NB at the significance level of α = 0.1.”
BackgroundMethodResultInfluential
Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification
Shin, Y., Lee, S., Ahn, M., Cho, H., Jun, S. C., & Lee, H.-N. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.05.007 · Cited by 73
Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications
Lotte, F. (2008) · HAL · Cited by 73
Self-citation
BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces
Simões, M., Borra, D., Santamaría-Vázquez, E., GBT-UPM, Bittencourt-Villalpando, M., Krzemiński, D., Miladinović, A., Neural_Engineering_Group, Schmid, T., Zhao, H., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.568104 · Cited by 72
EEG classification of adolescents with type I and type II of bipolar disorder
Khaleghi, A., Sheikhani, A., Mohammadi, M. R., Nasrabadi, A. M., Vand, S. R., Zarafshan, H., & Moeini, M. (2015) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-015-0375-0 · Cited by 72
Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface
Corsi, M., Chávez, M., Schwartz, D., Hugueville, L., Khambhati, A. N., Bassett, D. S., & Fallani, F. D. V. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500144 · Cited by 73
Filter Bank Convolutional Neural Network for Short Time-Window Steady-State Visual Evoked Potential Classification
Ding, W., Shan, J., Fang, B., Wang, C., Sun, F., & Li, X. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3132162 · Cited by 78
Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system
Lee, H. S., Schreiner, L., Jo, S.-H., Sieghartsleitner, S., Jordan, M., Pretl, H., Guger, C., & Park, H. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1009878 · Cited by 74
Sliding HDCA: Single-Trial EEG Classification to Overcome and Quantify Temporal Variability
Marathe, A. R., Ries, A. J., & McDowell, K. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2304884 · Cited by 72
Common Spatial Pattern revisited by Riemannian geometry
Barachant, A., Bon, S., Congedo, M., & Christian Jutten, B. (2010) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2010.5662067 · Cited by 72
Self-citation
Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces
Bashashati, H., Ward, R. K., Birch, G. E., & Bashashati, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129435 · Cited by 70
EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands
Pan, C., Shi, C., Mu, H., Li, J., & Gao, X. (2020) · Applied Sciences · doi:10.3390/app10051619 · Cited by 75
Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices
Trambaiolli, L. R., Kohl, S. H., Linden, D. E. J., & Mehler, D. M. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.015 · Cited by 81
Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications
Hermosilla, D. M., Codorniú, R. T., Baracaldo, R. L., Zamora, R. S., Delisle-Rodríguez, D., Albuerne, Y. L., & Álvarez, J. R. N. (2021) · IEEE Access · doi:10.1109/access.2021.3091399 · Cited by 78
Exploring dimensionality reduction of EEG features in motor imagery task classification
García-Laencina, P. J., Rodríguez-Bermúdez, G., & Roca‐Dorda, J. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.02.043 · Cited by 70
Neural decoding of expressive human movement from scalp electroencephalography (EEG)
Cruz-Garza, J. G., Hernandez, Z., Nepaul, S., Bradley, K. K., & Contreras-Vidal, J. L. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00188 · Cited by 71
A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome
Combaz, A., Chatelle, C., Robben, A., Vanhoof, G., Goeleven, A., Thijs, V., Hulle, M. M. V., & Laureys, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073691 · Cited by 71
Automatic Classification of Athletes With Residual Functional Deficits Following Concussion by Means of EEG Signal Using Support Vector Machine
Cao, C., Tutwiler, R. L., & Slobounov, S. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.918422 · Cited by 71
Brain-computer interface using P300 and virtual reality: A gaming approach for treating ADHD
Rohani, D. A., Sørensen, H. B. D., & Puthusserypady, S. K. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944403 · Cited by 71
Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method
Bashar, S. K., & Bhuiyan, M. I. H. (2016) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2016.04.009 · Cited by 69
Mining biometric data to predict programmer expertise and task difficulty
Lee, S., Hooshyar, D., Ji, H., Nam, K., & Lim, H. (2017) · Cluster Computing · doi:10.1007/s10586-017-0746-2 · Cited by 75
Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study
Khan, M. J., Ghafoor, U., & Hong, K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00479 · Cited by 73
EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review
Amer, N. S., & Belhouari, S. B. (2023) · IEEE Access · doi:10.1109/access.2023.3341419 · Cited by 80
Brain Computer Interface: A Review
Fouad, M. M., Amin, K. M., El-Bendary, N., & Hassanien, A. E. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10978-7_1 · Cited by 69
Bayesian Learning for Spatial Filtering in an EEG-Based Brain–Computer Interface
Zhang, H., Yang, H., & Guan, C. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2249087 · Cited by 71
Classification of EEG for Affect Recognition: An Adaptive Approach
AlZoubi, O., Calvo, R. A., & Stevens, R. H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10439-8_6 · Cited by 69
Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance
Corbet, T., Iturrate, I., Pereira, M., Perdikis, S., & Millán, J. D. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.005 · Cited by 76
“Even if EEG-based BMI are very promising, despite of years of research to improve decoding algorithms (Lotte et al., 2007) and the development of adaptive systems (Naros and Gharabaghi), they are still limited by the poor reliability and stability of decoders (Blankertz et al., 2010; Lotte et al.,…”
Selection of Features and Classifiers for EMG-EEG-Based Upper Limb Assistive Devices—A Review
Khan, S. M., Khan, A. A., & Farooq, O. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2019.2950897 · Cited by 69
Common Spatial Pattern Reformulated for Regularizations in Brain–Computer Interfaces
Wang, B., Wong, C. M., Kang, Z., Liu, F., Shui, C., Wan, F., & Chen, C. L. P. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2982901 · Cited by 73
EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation
Al-Qazzaz, N. K., Alyasseri, Z. A. A., Abdulkareem, K. H., Ali, N. S., Al‐Mhiqani, M. N., & Guger, C. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104799 · Cited by 70
EEG Feature Extraction and Data Augmentation in Emotion Recognition
Kalashami, M. P., Pedram, M. M., & Sadr, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7028517 · Cited by 69
“Many methods have been proposed to improve performance in many aspects, including preprocessing, feature extraction, feature selection, and classification [3, 4].”
Method
Random Forest and Filter Bank Common Spatial Patterns for EEG-Based Motor Imagery Classification
Bentlemsan, M., Zemouri, E.-T., Bouchaffra, D., Yahya-Zoubir, B., & Ferroudji, K. (2014) · International Conference on Intelligent Systems, Modelling and Simulation · doi:10.1109/isms.2014.46 · Cited by 69
A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems
Wright, J. C., Macefield, V. G., André, V. S., & Tapson, J. C. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00312 · Cited by 71
A New Generation of Brain-Computer Interface Based on Riemannian Geometry
Congedo, M., Barachant, A., & Andreev, A. (2013) · arXiv · doi:10.48550/arxiv.1310.8115 · Cited by 69
Self-citation
A novel statistical algorithm for multiclass EEG signal classification
Siuly, S., & Li, Y. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.05.011 · Cited by 68
Automatic and Adaptive Classification of Electroencephalographic Signals for Brain Computer Interfaces
Rodríguez-Bermúdez, G., & García-Laencina, P. J. (2012) · Journal of Medical Systems · doi:10.1007/s10916-012-9893-4 · Cited by 68
Empathy, motivation, and P300 BCI performance
Kleih, S. C., & Kübler, A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00642 · Cited by 72
Cross-association analysis of EEG and EMG signals according to movement intention state
Kim, B., Kim, L., Kim, Y., & Yoo, S. K. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.02.001 · Cited by 68
Coherence resonance in neural networks: Theory and experiments
Pisarchik, A. N., & Hramov, A. E. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.11.004 · Cited by 67
A review on transfer learning for brain-computer interface classification
Wang, P., Lu, J., Zhang, B., & Tang, Z. (2015) · International Conference on Information Science and Technology · doi:10.1109/icist.2015.7288989 · Cited by 66
EEG-based detection of emotional valence towards a reproducible measurement of emotions
Apicella, A., Arpaïa, P., Mastrati, G., & Moccaldi, N. (2021) · Scientific Reports · doi:10.1038/s41598-021-00812-7 · Cited by 71
Towards Inexpensive BCI Control for Wheelchair Navigation in the Enabled Environment – A Hardware Survey
Stamps, K., & Hamam, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15314-3_32 · Cited by 66
Feature Selection for Brain-Computer Interfaces
Koprinska, I. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14640-4_8 · Cited by 66
Movement Detection of Human Body Segments: Passive radio-frequency identification and machine-learning technologies
Amendola, S., Bianchi, L., & Marrocco, G. (2015) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2015.2437274 · Cited by 66
Spatially Regularized Common Spatial Patterns for EEG Classification
Lotte, F., & Guan, C. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.904 · Cited by 66
Self-citation
SPEAK YOUR MIND! Towards Imagined Speech Recognition with Hierarchical Deep Learning
Saha, P., Abdul-Mageed, M., & Fels, S. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3041 · Cited by 65
Dynamically weighted ensemble classification for non-stationary EEG processing
Liyanage, S. R., Guan, C., Zhang, H., Ang, K. K., Xu, J., & Lee, T. H. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036007 · Cited by 65
Exploring two novel features for EEG-based brain–computer interfaces: Multifractal cumulants and predictive complexity
Brodu, N., Lotte, F., & Lécuyer, A. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.10.010 · Cited by 65
Self-citation
A Single-Channel EOG-Based Speller
He, S., & Li, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2716109 · Cited by 66
Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index
Nuamah, J. K., & Seong, Y. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1338012 · Cited by 65
Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient
Han, C. H., Kim, Y.-W., Kim, D. Y., Kim, S. H., Nenadić, Z., & Im, C. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0493-0 · Cited by 64
Independent Component Ensemble of EEG for Brain–Computer Interface
Chuang, C., Ko, L., Lin, Y., Jung, T., & Lin, C. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2293139 · Cited by 64
Multiclass support matrix machine for single trial EEG classification
Zheng, Q., Zhu, F., Qin, J., & Heng, P. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.030 · Cited by 64
SEREEGA: Simulating event-related EEG activity
Krol, L. R., Pawlitzki, J., Lotte, F., Gramann, K., & Zander, T. O. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.08.001 · Cited by 64
Self-citation
Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies
Hussain, L., Saeed, S., Awan, I. A., Idris, A., Nadeem, M. S. A., & Chaudhry, Q.-U.-A. (2019) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405614666180718123533 · Cited by 63
Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used
Ajenaghughrure, I. B., Sousa, S., & Lamas, D. (2020) · Multimodal Technologies and Interaction · doi:10.3390/mti4030063 · Cited by 63
“8% of the 51 primary included studies assessing trust with psychophysiological signals used ensemble techniques to overcome the limitations of previous dynamic algorithms by combining two or more dynamic algorithms to enhance the accuracy of the predictions in the classification or regression problems associated with…”
Background
Shopper intent prediction from clickstream e-commerce data with minimal browsing information
Requena, B., Cassani, G., Tagliabue, J., Greco, C., & Lacasa, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-73622-y · Cited by 70
The effect of multimodal and enriched feedback on SMR-BCI performance
Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J. H., Murray‐Smith, R., Leeb, R., Millán, J. D. R., & Kübler, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.06.004 · Cited by 63
Training leads to increased auditory brain–computer interface performance of end-users with motor impairments
Halder, S., Käthner, I., & Kübler, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.08.007 · Cited by 68
An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface
Xu, P., Yang, P., Lei, X., & Yao, D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014634 · Cited by 63
Single trial recognition of error-related potentials during observation of robot operation
Iturrate, I., Montesano, L., & Mínguez, J. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627380 · Cited by 63
NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals
Kasabov, N. K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33212-8_21 · Cited by 64
Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis
Wang, Y., Wang, Y.-T., & Jung, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037665 · Cited by 63
Online classification of imagined speech using functional near-infrared spectroscopy signals
Sereshkeh, A. R., Yousefi, R., Wong, A. T., & Chau, T. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae4b9 · Cited by 62
“Regularization can be necessary in classification models to preserve generalizability, especially when the number of samples are of the same order of magnitude as the number of features [54].”
Background
A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications
Rasheed, S. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040042 · Cited by 63
Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique
Djemal, R., Baziyad, A. G., Belwafi, K., Gannouni, S., & Kaaniche, W. (2016) · Brain Sciences · doi:10.3390/brainsci6030036 · Cited by 62
Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces
Leeuwis, N., Yoon, S., & Alimardani, M. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.732946 · Cited by 69
“…(Thompson, 2019); the issue of BCI inefficiency arises with the variations in brain signals between different subjects and experiments (Lotte et al., 2007; Lee et al., 2019a) and its prevalence has been investigated by multiple studies as well (Jeunet et al., 2016; Lee et al., 2019a;…”
Background
Adaptation in P300 Brain–Computer Interfaces: A Two-Classifier Cotraining Approach
Panicker, R. C., Puthusserypady, S. K., & Sun, Y. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2058804 · Cited by 61
Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI
Tariq, M., Trivailo, P. M., & Simić, M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230184 · Cited by 61
“These efficient results could be due to the low dimensional feature vectors deployed in the study for a BCI [26].”
BackgroundMethodInfluential
P300 Brain–Computer Interface-Based Drone Control in Virtual and Augmented Reality
Kim, S., Lee, S.-Y., Kang, H., Kim, S., & Ahn, M. (2021) · Sensors · doi:10.3390/s21175765 · Cited by 61
ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data
Ali, O., Saif-ur-Rehman, M., Glasmachers, T., Iossifidis, I., & Klaes, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107649 · Cited by 73
Classification of EEG Signals in a Brain-Computer Interface System
Larsen, E. (2011) · Cited by 60
Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders
Broccard, F. D., Mullen, T. R., Chi, Y. M., Peterson, D., Iversen, J. R., Arnold, M., Kreutz-Delgado, K., Jung, T., Makeig, S., Howard Poizner, A., et al. (2014) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1032-6 · Cited by 62
Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification
Díez, P., Mut, V. A., Laciar, E., Torres, A., & Avila, E. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5335278 · Cited by 60
EEG-Based Brain-Computer Interfaces
Wang, Y., Nakanishi, M., & Zhang, D. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-2050-7_2 · Cited by 63
Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI
Tang, Z., Li, C., Wu, J., Liu, P., & Cheng, S. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800083 · Cited by 57

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

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

A review of classification algorithms for EEG-based brain–computer interfaces

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks