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Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems
Abstract
Brain-computer interface (BCI) technology relies on the ability of individuals to voluntarily and reliably produce changes in their electroencephalographic (EEG) activity. The present paper reviews research on cognitive tasks and other methods of generating and controlling specific changes in EEG activity that can be used to drive BCI systems. To date, motor imagery has been the most commonly used task. This paper explores the possibility that other cognitive tasks, including those used in imaging studies, may prove to be more effective. Other factors which influence performance are also considered in relation to selection of tasks, as well as training of subjects.
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“Interestingly, users have reported reaching a state of proficiency through learning where BCI control becomes “automatic,” as they no longer need to engage explicitly in MI [23,40–42,50,60,61].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [60]
Citations
Cites
Gevins, A. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.4.374 · Cited by 1,623
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117
Spatial filter selection for EEG-based communication
McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937
The neurophysiological basis of motor imagery
Decety, J. (1996) · Behavioural Brain Research · doi:10.1016/0166-4328(95)00225-1 · Cited by 842
Direct control of a computer from the human central nervous system
Kennedy, P. R., Bakay, R. A. E., Moore, M. M., Adams, K. D., & Goldwaithe, J. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847815 · Cited by 573
Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G. (1998) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872098779480578 · Cited by 535
Current trends in Graz brain-computer interface (BCI) research
Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoser, H., Schlögl, A., Obermaier, B., & Pregenzer, M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847821 · Cited by 518
Brain-Computer Interface—a new communication device for handicapped persons
Pfurtscheller, G., Flotzinger, D., & Kalcher, J. (1993) · Journal of Microcomputer Applications · doi:10.1006/jmca.1993.1030 · Cited by 502
The thought translation device (TTD) for completely paralyzed patients
Birbaumer, N., Kubler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., & Flor, H. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847812 · Cited by 493
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Zapała, D., Francuz, P., Zapała, E., Kopiś-Posiej, N., Wierzgała, P., Augustynowicz, P., Majkowski, A., & Kołodziej, M. (2017) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-017-9383-z · Cited by 46
Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity
Wriessnegger, S. C., Brunner, C., & Muller-Putz, G. R. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01976 · Cited by 44
Wireless sEMG-Based Body–Machine Interface for Assistive Technology Devices
Fall, C. L., Gagnon-Turcotte, G., Dube, J.-F., Gagne, J. S., Delisle, Y., Campeau‐Lecours, A., Gosselin, C., & Gosselin, B. J. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2642837 · Cited by 43
Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand
Xiao, R., & Ding, L. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/243257 · Cited by 41
Friedrich, E. V. C., Scherer, R., & Neuper, C. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.05.020 · Cited by 41
Xie, Y., & Oniga, S. (2023) · Sensors · doi:10.3390/s23041932 · Cited by 40
Using Brain Computer Interface for Synthesized Speech Communication for the Physically Disabled
Soman, S., & Murthy, B. K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.02.023 · Cited by 38
Rozado, D., Duenser, A., & Howell, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121262 · Cited by 38
González-Franco, M., Yuan, P., Zhang, D., Hong, B., & Gao, S. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091560 · Cited by 38
Das, A., Sundaram, S., & Sundararajan, N. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.007 · Cited by 37
Venthur, B., Blankertz, B., Gugler, M. F., & Curio, G. (2010) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2010.5641772 · Cited by 36
Hazard differentiation embedded in the brain: A near-infrared spectroscopy-based study
Zhou, X., Hu, Y., Liao, P., & Zhang, D. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103473 · Cited by 36
Tang, H., Dai, M., Du, X., Hung, J.-L., & Li, H. (2023) · Distance Education · doi:10.1080/01587919.2023.2209025 · Cited by 35
Effects of Soft Drinks on Resting State EEG and Brain–Computer Interface Performance
Meng, J., Mundahl, J., Streitz, T. D., Maile, K., Gulachek, N., He, J., & He, B. (2017) · IEEE Access · doi:10.1109/access.2017.2751069 · Cited by 35
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Llera, A., Gómez, V., & Kappen, H. J. (2014) · Neural Computation · doi:10.1162/neco_a_00592 · Cited by 34
Azab, A. M., Ahmadi, H., Mihaylova, L. S., & Arvaneh, M. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab64a0 · Cited by 34
The Extreme Energy Ratio Criterion for EEG Feature Extraction
Sun, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87559-8_95 · Cited by 33
Adaptive estimation of EEG-rhythms for optimal band identification in BCI
Veluvolu, K. C., Wang, Y., & Kavuri, S. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.08.035 · Cited by 33
Free-View, 3D Gaze-Guided, Assistive Robotic System for Activities of Daily Living
Wang, M., Kogkas, A., Darzi, A. W., & Mylonas, G. P. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594045 · Cited by 33
Guttmann-Flury, E., Sheng, X., & Zhu, X. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01897-2 · Cited by 33
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
Source-based artifact-rejection techniques for TMS–EEG
Mutanen, T. P., Metsomaa, J., Makkonen, M., Varone, G., Marzetti, L., & Ilmoniemi, R. J. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109693 · Cited by 32
Weyand, S., Takehara‐Nishiuchi, K., & Chau, T. T. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2399392 · Cited by 34
Shin, J., Kim, D.-W., Müller, K., & Hwang, H.-J. (2018) · Sensors · doi:10.3390/s18061827 · Cited by 32
Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue
Talukdar, U., Hazarika, S. M., & Gan, J. Q. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab53f1 · Cited by 32
Emerging Frontiers in Human–Robot Interaction
Safavi, F., Olikkal, P., Pei, D., Kamal, S., Meyerson, H., Penumalee, V., & Vinjamuri, R. K. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02074-7 · Cited by 33
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
Fireworks algorithm framework for Big Data optimization
Majdouli, M. A. E., Rbouh, I., Bougrine, S., Elbenani, B., & Imrani, A. A. E. (2016) · Memetic Computing · doi:10.1007/s12293-016-0201-6 · Cited by 31
Park, Y., & Chung, W. (2019) · Sensors · doi:10.3390/s19173769 · Cited by 31
Self-health monitoring and wearable neurotechnologies
Cannard, C., Brandmeyer, T., Wahbeh, H., & Arnaud Delorme, A. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00016-0 · Cited by 35
Meisheri, H., Ramrao, N., & Mitra, S. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.09046 · Cited by 31
Friedrich, E. V. C., Scherer, R., Sonnleitner, K., & Neuper, C. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.03.019 · Cited by 30
Hilbert transform-based event-related patterns for motor imagery brain computer interface
Bagh, N., & Reddy, M. R. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102020 · Cited by 30
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Müller, K. R., Krauledat, M., Dornhege, G., Curio, G., & Blankertz, B. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73345-4_80 · Cited by 30
Shin, J., & Chung, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3292909 · Cited by 30
A review of methods and applications of brain computer interface systems
Alamdari, N., Haider, A., Arefin, R., Verma, A. K., Tavakolian, K., & Fazel-Rezai, R. (2016) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2016.7535263 · Cited by 29
Brain–computer interfaces for patients with disorders of consciousness
Gibson, R. M., Owen, A. M., & Cruse, D. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.04.003 · Cited by 29
Tomioka, R., Dornhege, G., Nolte, G., Blankertz, B., & Aihara, K. (2006) · Cited by 28
Widge, A. S., & Moritz, C. T. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/024001 · Cited by 29
An optimal kernel feature extractor and its application to EEG signal classification
Sun, S., & Zhang, C. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.125 · Cited by 27
Predicting the Performance of Motor Imagery in Stroke Patients
Park, C.-H., Chang, W. H., Lee, M., Kwon, G. H., Kim, L., Kim, S. T., & Kim, Y. (2014) · Neurorehabilitation and neural repair · doi:10.1177/1545968314543308 · Cited by 26
Electrophysiological CNS-processes related to associative learning in humans
Christoffersen, G. R. J., & Schachtman, T. R. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.09.011 · Cited by 26
Arvaneh, M., Robertson, I. H., & Ward, T. E. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945117 · Cited by 26
Gibson, R. M., Chennu, S., Owen, A. M., & Cruse, D. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.11.034 · Cited by 26
Evolutionary perspective for optimal selection of EEG electrodes and features
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Localisation of cognitive tasks used in EEG-based BCIs
Dyson, M., Sepulveda, F., & Gan, J. Q. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.03.011 · Cited by 27
An efficient P300 speller system for Brain-Computer Interface
Chaurasiya, R. K., Londhe, N. D., & Ghosh, S. (2015) · International Conference on Signal Processing, Computing and Control (ISPCC) · doi:10.1109/ispcc.2015.7374998 · Cited by 25
Towards psychologically adaptive brain–computer interfaces
Myrden, A., & Chau, T. T. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/6/066022 · Cited by 25
A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task
Kim, H.-J., Lee, M.-H., & Lee, M. (2020) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci48061.2020.9061634 · Cited by 25
Das, A., & Suresh, S. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.545 · Cited by 24
Trambaiolli, L. R., Biazoli, C. E., Cravo, A. M., Falk, T. H., & Sato, J. R. (2018) · Neurophotonics · doi:10.1117/1.nph.5.3.035009 · Cited by 23
Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method
Hooda, N., & Kumar, N. (2019) · IETE Technical Review · doi:10.1080/02564602.2019.1620138 · Cited by 23
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior
Stiso, J., Corsi, M., Vettel, J. M., Garcia, J. O., Pasqualetti, F., Fallani, F. D. V., Lucas, T. H., & Bassett, D. S. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab9064 · Cited by 22
Extreme energy difference for feature extraction of EEG signals
Sun, S. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.11.064 · Cited by 21
Evolving spatial and frequency selection filters for Brain-Computer Interfaces
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Personal responsibility in the age of user-controlled neuroprosthetics
Brown, T., Moore, P., Herron, J. A., Thompson, M. C., Bonaci, T., Chizeck, H. J., & Goering, S. (2016) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics.2016.7560039 · Cited by 19
Analyzing text recognition from tactually evoked EEG
Khasnobish, A., Datta, S., Bose, R., Tibarewala, D. N., & Konar, A. (2017) · Cognitive Neurodynamics · doi:10.1007/s11571-017-9452-2 · Cited by 19
Izzuddin, T. A., Safri, N. M., & Othman, M. A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05393-6 · Cited by 19
Brain–Computer Interfaces for Communication and Control in Locked-in Patients
Nijboer, F., & Broermann, U. (2009) · The frontiers collection · doi:10.1007/978-3-642-02091-9_11 · Cited by 18
Borgheai, S. B., Zisk, A. H., McLinden, J., McIntyre, J., Sadjadi, R., & Shahriari, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107658 · Cited by 18
Effect of subject training on a movement-related cortical potential-based brain-computer interface
Jochumsen, M., Niazi, I., Nedergaard, R. W., Navid, M. S., & Dremstrup, K. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.11.012 · Cited by 19
The Berlin Brain-Computer Interface: Report from the Feedback Sessions
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K., & Curio, G. (2005) · Cited by 17
Motor imagery EEG signal classification using upper triangle filter bank auto-encode method
Tang, R., Li, Z., & Xie, X. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102608 · Cited by 16
Xiong, W. H., & Wei, Q. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263641 · Cited by 16
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Alharbi, H. F. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/2793211 · Cited by 16
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Multiple graph fusion based on Riemannian geometry for motor imagery classification
Xie, X., Zou, X., Yu, T., Tang, R., Hou, Y., & Qi, F. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02975-2 · Cited by 16
Motor imagery EEG detection by empirical mode decomposition
Xiaojing, G., Wu, X., & Dexiang, Z. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634164 · Cited by 16
Singh, K., Lin, C.-C., Huang, W., Lei, W., Chiueh, H., Wang, Y., Chang, P.-H., Lin, R.-Z., & Huang, W.-C. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c23013 · Cited by 15
Park, Y., & Chung, W. (2020) · IEEE Access · doi:10.1109/access.2020.2993318 · Cited by 15
Data synthesis method preserving correlation of features
Yang, W., & Nam, W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108241 · Cited by 15
Selection of the best mental tasks for a SVM-based BCI system
Hortal, E., Iáñez, E., Úbeda, A., Planelles, D., Costa, Á., & Azorín, J. M. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974125 · Cited by 15
Schneider, C., Pereira, M., Tonin, L., & Millán, J. D. R. (2019) · Brain Topography · doi:10.1007/s10548-019-00725-9 · Cited by 18
Movement-related cortical potentials and their application in brain–computer interfacing
Dremstrup, K., Gu, Y., Nascimento, O. F. D., & Farina, D. (2013) · Wiley eBooks · doi:10.1002/9781118628522.ch13 · Cited by 13
Phanikrishna, B. V. (2021) · doi:10.36227/techrxiv.16691605.v1 · Cited by 15
Schudlo, L. C., & Chau, T. T. (2017) · International Journal of Neural Systems · doi:10.1142/s0129065717500526 · Cited by 14
EEG as a marker of brain plasticity in clinical applications
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The Training Issue in Brain-Computer Interface: A Multi-disciplinary Field
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EEG Based BCI Using Visual Imagery Task for Robot Control
Azmy, H., & Safri, N. M. (2013) · Jurnal Teknologi · doi:10.11113/jt.v61.1628 · Cited by 14
Making brain-computer interfaces better : improving usability through post-processing
Bos, D. P.-O. (2014) · doi:10.3990/1.9789036537797 · Cited by 14
Wei, Q., Li, C., Wang, Y., & Gao, X. (2025) · Scientific Reports · doi:10.1038/s41598-024-84534-6 · Cited by 13
What are the Causes of Performance Variation in Brain-Computer Interfacing?
Grosse‐Wentrup, M. (2011) · Cited by 13
Detecting Intention Through Motor-Imagery-Triggered Pupil Dilations
Rozado, D., Lochner, M., Engelke, U., & Duenser, A. (2017) · Human-Computer Interaction · doi:10.1080/07370024.2017.1293540 · Cited by 13
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Mathur, N., Gupta, A., Jaswal, S., & Verma, R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102996 · Cited by 13
Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs
Ying, J., Wei, Q., & Zhou, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-14026-y · Cited by 13
Designing Future BCIs: Beyond the Bit Rate
Quek, M., Höhne, J., Murray‐Smith, R., & Tangermann, M. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_9 · Cited by 13
Novel hold–release functionality in a P300 brain–computer interface
Alcaide-Aguirre, R. E., & Huggins, J. E. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/6/066010 · Cited by 13
Transition Detection for Brain Computer Interface Classification
Aler, R., Galván, I. M., & Valls, J. M. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-11721-3_15 · Cited by 13
Model analyses of visual biofeedback training for EEG-based brain-computer interface
Chen, C., Ju, M., Sun, Y.-N., & Lin, C. K. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0148-4 · Cited by 12
Driver Lane Change Prediction Using Physiological Measures
Murphey, Y., Kochhar, D., Watta, P., Wang, X., & Wang, T. (2015) · SAE International Journal of Transportation Safety · doi:10.4271/2015-01-1403 · Cited by 11
Short-Term Kinesthetic Training for Sensorimotor Rhythms: Effects in Experts and Amateurs
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Sequential classification of mental tasks vs. idle state for EEG based BCIs
Dyson, M., Sepulveda, F., Gan, J. Q., & Roberts, S. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109305 · Cited by 12
Multiclass common spatial pattern with artifacts removal methodology for EEG signals
Meisheri, H., Ramrao, N., & Mitra, S. K. (2016) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi.2016.7743264 · Cited by 12
Improving the quality of a collective signal in a consumer EEG headset
Morán, A., & Soriano, M. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197597 · Cited by 13
Classifying Alcoholics and Control Patients Using Deep Learning and Peak Visualization Method
Fayyaz, A., Maqbool, M., & Saeed, M. (2019) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1145/3387168.3389119 · Cited by 12
BCI-teleoperated androids; a study of embodiment and its effect on motor imagery learning
Alimardani, M., Nishio, S., & Ishiguro, H. (2015) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines.2015.7329753 · Cited by 12
Energy feature extraction of EEG signals and a case study
Li, J., & Sun, S. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634126 · Cited by 12
Xu, K., Huang, Y.-Y., & Duann, J. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00302 · Cited by 14
Metadata
- Authors
- Curran, E.
- Affiliations
- Royal Hospital for Neuro-disability (GB)
- Corresponding author
- Curran, E
- Venue
- Brain and Cognition
- Publisher
- Elsevier BV
- Published
- 2003-04
- Volume
- 51
- Issue
- 3
- Pages
- 326-336
- ISSN
- 0278-2626 (ISSN-L)
- OpenAlex
- W2102757742
- Cited by
- 417
- References
- 36
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 7 · 2023: 19 · 2022: 11 · 2021: 27 · 2020: 25 · 2019: 32 · 2018: 24 · 2017: 28 · 2016: 22 · 2015: 31 · 2014: 27 · 2013: 26 · 2012: 18
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Brain and Cognition (publishedVersion)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
- Keywords
- brain-computer interface, EEG signal generation, motor imagery, cognitive tasks, task selection
- MeSH terms
- Brain, Electroencephalography, Humans, Learning, Magnetic Resonance Imaging, 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 ›
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