Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Open accessAbstract
Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.
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Most current BCI task paradigms use simple cued center-out tasks made of discrete trials.
“Current BCI task paradigms overwhelmingly involve simple cued center-out tasks defined by discrete trials (DT) of neural control (18).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [18]
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 [40]
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 [40]
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 [40]
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Eliseyev, A., Auboiroux, V., Costecalde, T., Langar, L., Charvet, G., Mestais, C., Aksenova, T., & Benabid, A.-L. (2017) · Scientific Reports · doi:10.1038/s41598-017-16579-9 · Cited by 42 · see citing works
Single-trial EEG RSVP classification using convolutional neural networks
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Toward Optimal Target Placement for Neural Prosthetic Devices
Cunningham, J. P., Yu, B. M., Gilja, V., Ryu, S. I., & Shenoy, K. V. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90833.2008 · Cited by 25 · see citing works
- Authors
- Wolpaw, J. R., & McFarland, D. J.
- Affiliations
- New York State Department of Health (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2004-12-07
- Volume
- 101
- Issue
- 51
- Pages
- 17849-17854
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC535103
- OpenAlex
- W2103494817
- Cited by
- 1,512
- References
- 35
- Citations by year
- 2026: 19 · 2025: 31 · 2024: 44 · 2023: 31 · 2022: 34 · 2021: 69 · 2020: 62 · 2019: 69 · 2018: 75 · 2017: 70 · 2016: 76 · 2015: 81 · 2014: 107 · 2013: 108 · 2012: 95
- FWCI
- 27.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, R01EB000856)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- non-invasive brain-computer interface, EEG, point-to-point control, adaptive algorithms, spinal cord injury, motor impairment, neural prostheses, robotic arm control
- MeSH terms
- Adult, Algorithms, Brain, Female, Humans, Male, Man-Machine Systems, Movement, Robotics, Signal Processing, Computer-Assisted, 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.
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