The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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
Cited by
Sorted by citations
Items 401–416 of 416
A Study on Changes in Estimation Accuracy for EEG Data During Calibration and Operation in MI-BCI
Kanda, T., Isezaki, T., & Okitsu, K. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782616
Goswami, S., Phelan, J., Anderson, S., & Arvaneh, M. (2024) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine62247.2024.10795940
Zhou, X., Menassa, C. C., & Kamat, V. R. (2025) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2620
IMPROVING CLASSIFICATION FOR BRAIN COMPUTER INTERFACES USING TRANSITIONS AND A MOVING WINDOW
Aler, R., Galván, I. M., & Valls, J. M. (2009) · International Conference on Bio-inspired Systems and Signal Processing · doi:10.5220/0001535800650071
Baboukani, P. S., Ordoñez, R., Gravesen, C., Østergaard, J., Rank, M. L., Aličković, E., & Cabrera, A. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.13.711289
Brain and gaze based wheelchair navigation enhancement
Lamti, H. A. (2013) · theses.fr (ABES) · doi:10.70675/78c30f0ezb40ez4da6zbd5fzccafc83820c9
EEG correlates of well-being and wearable EEG
Cannard, C. (2022) · theses.fr (ABES) · doi:10.70675/df2fc223ze70cz4c74zbdeez427a8788428b
Patashov, D., Liu, L. H., Tominaga, J., Nakajima, K., Miyanaga, H., Tsunematsu, S., Kato, T., Tanaka, K., & Sakai, H. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04093-x
Decoding Cognitive Flexibility with EEG in Individuals with Depression and Suicide Attempts
Sun, X., Ye, X., Gong, R., Doose, J., Chang, C., Ping, C., Hashrmpour, S., Eade, J., Williams, A., Doyle, G., et al. (2025) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner61569.2025.11589052
Fu, Y., Yan, L., Chen, X., Pan, J., Wang, F., Li, T., Zhao, L., Yang, X., Luo, R., Xu, J., et al. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1964902
Abunasar, M. D., Verma, D. K., Rana, V., Bhardwaj, V., & Yusuf, A. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36382-5.00013-1
Single trial analyses of novel cognitive tasks in an EEG-based Brain-Computer Interface framework
Evans, N. J. (2008) · Infoscience (Ecole Polytechnique Fédérale de Lausanne)
A Novel Adaptive Method for Robotic Wheelchair Navigation Using Brain Signals
Mano, M. (2013) · Institutional Repositories DataBase (IRDB) · doi:10.15099/00005168
Neurofeedback: Challenges, Applications, and Opportunities for Education
Chan, S. Y. S. (2015) · Summit (Simon Fraser University)
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 ›
Are you an author, or do you see a problem with this page? Request a review
