A high-performance neural prosthesis enabled by control algorithm design
Open accessAbstract
Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer individuals with disabilities greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower, with less accurate control, than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that incorporates assumptions about the nature of closed-loop neural prosthetic control. When tested in rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperformed existing neural prosthetic algorithms in all measured domains and halved target acquisition time. This control algorithm permits sustained, uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses.
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This hover period is not widely used with non-invasive BCIs but is commonly used with invasive BCIs.
“This hover period is not widely used with non-invasive BCIs but is commonly used with invasive BCIs25.”
From Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks (2016) · cites this paper as [25]
The Results (Performance Overview; page unavailable) describe an invasive neural-control task in which monkeys had to hold a cursor within a target box for 500 ms, supporting an example of a hover period. The paper does not establish that hover periods are commonly used across invasive BCIs or uncommon in non-invasive BCIs. (LLM, conf=0.86)
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- Authors
- Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., & Shenoy, K. V.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- University of Cambridge (GB)
- Gatsby Computational Neuroscience Unit (GB)
- Palo Alto Medical Foundation (US)
- Corresponding author
- Gilja, Vikash
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2012-11-18
- Volume
- 15
- Issue
- 12
- Pages
- 1752-1757
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3638087
- OpenAlex
- W2166422604
- Cited by
- 564
- References
- 45
- Citations by year
- 2026: 26 · 2025: 43 · 2024: 41 · 2023: 29 · 2022: 33 · 2021: 36 · 2020: 49 · 2019: 42 · 2018: 47 · 2017: 44 · 2016: 44 · 2015: 32 · 2014: 63 · 2013: 33
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (DP1OD006409)
- Engineering and Physical Sciences Research Council (EP/H019472/1)
- National Institute of Neurological Disorders and Stroke (R01NS054283)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP1HD075623)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- neural prostheses, brain-computer interface, cursor control, Kalman filter, motor cortex, feedback control, rhesus monkeys, long-term implant performance
- MeSH terms
- Algorithms, Animals, Electrodes, Implanted, Macaca, Male, Photic Stimulation, Psychomotor Performance, Neural Prostheses
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 ›
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