期刊
NATURE NEUROSCIENCE
卷 15, 期 12, 页码 1752-1757出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/nn.3265
关键词
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资金
- US National Defense Science and Engineering Graduate Fellowship
- National Science Foundation Graduate Research Fellowships
- Stanford Medical Scholars Program
- Howard Hughes Medical Institute Medical Research Fellows Program
- Paul and Daisy Soros Fellowship
- Stanford Medical Scientist Training Program
- Stanford Graduate Fellowship
- Gatsby Charitable Foundation
- Helen Hay Whitney postdoctoral fellowship
- Burroughs Welcome Fund Career Awards in the Biomedical Sciences
- Christopher and Dana Reeve Paralysis Foundation
- Defense Advanced Research Projects Agency [N66001-06-C-8005, N66001-10-C-2010]
- US National Institutes of Health, National Institute of Neurological Disorders and Stroke Collaborative Research in Computational Neuroscience [R01-NS054283]
- National Institutes of Health Directors Pioneer Award [1DP1OD006409]
- Engineering and Physical Sciences Research Council [EP/H019472/1] Funding Source: researchfish
- EPSRC [EP/H019472/1] Funding Source: UKRI
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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