Emulation of computer mouse control with a noninvasive brain–computer interface
Open accessAbstract
Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.
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Direct evidence of learned SMR modulation at the BCI feature level is rare or incomplete, derived in able-bodied populations and not longitudinal.
“Direct evidence of learned SMR modulation at the BCI feature level is, in fact, rare or incomplete, derived in able-bodied populations and not longitudinal [10,23,26,28,38–41].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [41]
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 [41]
Works where users acquired BCI control, in able-bodied and end-user populations, do not report any learning metric over time.
“Firstly, works where users acquired BCI control (able-bodied [3,25] and end-users [40,41,51,62,63]) do not report any learning metric over time.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [41]
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- Authors
- McFarland, D. J., Krusienski, D. J., Sarnacki, W. A., & Wolpaw, J. R.
- Affiliations
- New York State Department of Health (US)
- University of North Florida (US)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2008-03-05
- Volume
- 5
- Issue
- 2
- Pages
- 101-110
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC2757111
- OpenAlex
- W2157424831
- Cited by
- 202
- References
- 43
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 12 · 2021: 10 · 2020: 6 · 2019: 8 · 2018: 10 · 2017: 10 · 2016: 11 · 2015: 8 · 2014: 9 · 2013: 9 · 2012: 17
- FWCI
- 11.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- James S. McDonnell Foundation (EB00856, HD30146)
- National Institutes of Health (HD30146)
- National Institute of Neurological Disorders and Stroke (HD30146, EB00856)
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, HD30146, R01EB000856)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- National Center for Medical Rehabilitation Research
- 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 › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- non-invasive brain-computer interface, EEG, cursor movement, target selection, adaptive algorithms, spinal cord injury, motor impairment, neural prostheses, multidimensional control, assistive technology
- MeSH terms
- Adult, Algorithms, Brain, Communication Aids for Disabled, Communication Devices for People with Disabilities, Electrocardiography, Female, Humans, Male, Middle Aged, Spinal Cord Injuries, User-Computer Interface, Computer Peripherals, Evoked Potentials, Motor
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