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A High-Performance Neural Prosthesis Enabled by Control Algorithm Design
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 disabled patients greater interaction with the world. However, relatively low performance remains a critical barrier to successfu...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638087/ https://www.ncbi.nlm.nih.gov/pubmed/23160043 http://dx.doi.org/10.1038/nn.3265 |
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author | Gilja, Vikash Nuyujukian, Paul Chestek, Cindy A. Cunningham, John P. Yu, Byron M. Fan, Joline M. Churchland, Mark M. Kaufman, Matthew T. Kao, Jonathan C. Ryu, Stephen I. Shenoy, Krishna V. |
author_facet | Gilja, Vikash Nuyujukian, Paul Chestek, Cindy A. Cunningham, John P. Yu, Byron M. Fan, Joline M. Churchland, Mark M. Kaufman, Matthew T. Kao, Jonathan C. Ryu, Stephen I. Shenoy, Krishna V. |
author_sort | Gilja, Vikash |
collection | PubMed |
description | 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 disabled patients 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 with rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperforms existing neural prostheses in all measured domains and halves 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 across two monkeys, thereby increasing the clinical viability of neural prostheses. |
format | Online Article Text |
id | pubmed-3638087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
record_format | MEDLINE/PubMed |
spelling | pubmed-36380872013-06-01 A High-Performance Neural Prosthesis Enabled by Control Algorithm Design Gilja, Vikash Nuyujukian, Paul Chestek, Cindy A. Cunningham, John P. Yu, Byron M. Fan, Joline M. Churchland, Mark M. Kaufman, Matthew T. Kao, Jonathan C. Ryu, Stephen I. Shenoy, Krishna V. Nat Neurosci Article 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 disabled patients 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 with rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperforms existing neural prostheses in all measured domains and halves 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 across two monkeys, thereby increasing the clinical viability of neural prostheses. 2012-11-18 2012-12 /pmc/articles/PMC3638087/ /pubmed/23160043 http://dx.doi.org/10.1038/nn.3265 Text en Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Gilja, Vikash Nuyujukian, Paul Chestek, Cindy A. Cunningham, John P. Yu, Byron M. Fan, Joline M. Churchland, Mark M. Kaufman, Matthew T. Kao, Jonathan C. Ryu, Stephen I. Shenoy, Krishna V. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title | A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title_full | A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title_fullStr | A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title_full_unstemmed | A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title_short | A High-Performance Neural Prosthesis Enabled by Control Algorithm Design |
title_sort | high-performance neural prosthesis enabled by control algorithm design |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638087/ https://www.ncbi.nlm.nih.gov/pubmed/23160043 http://dx.doi.org/10.1038/nn.3265 |
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