Answering questions with an electroencephalogram-based brain-computer interface
Abstract
Objective To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions.Methods Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again.Results For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly.Conclusions The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.
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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 [61]
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Chaudhary, U. (2025) · Springer eBooks · doi:10.1007/978-3-031-78991-5_4
- Authors
- Miner, L. A., McFarland, D. J., & Wolpaw, J. R.
- Affiliations
- New York State Department of Health (US)
- Venue
- Archives of Physical Medicine and Rehabilitation
- Publisher
- Elsevier BV
- Published
- 1998-09
- Volume
- 79
- Issue
- 9
- Pages
- 1029-1033
- ISSN
- 0003-9993 (ISSN-L)
- OpenAlex
- W2088734134
- Cited by
- 85
- References
- 25
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 7 · 2017: 3 · 2016: 1 · 2015: 2 · 2014: 10 · 2013: 2 · 2012: 2
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Archives of Physical Medicine and Rehabilitation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- 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
- EEG-based brain-computer interface, answering questions, mu rhythm, beta oscillations, sensorimotor cortex, cursor control, response verification, amyotrophic lateral sclerosis, question complexity
- MeSH terms
- Adult, Aged, Attention, Biofeedback, Psychology, Brain Mapping, Communication Aids for Disabled, Communication Devices for People with Disabilities, Computer Systems, Electrodes, Electroencephalography, Female, Disabled Persons, Persons with Disabilities, Humans, Male, Middle Aged, Software, Somatosensory Cortex, User-Computer Interface
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