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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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.
“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]
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- 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
- Life Sciences › Neuroscience › Cognitive Neuroscience › 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
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