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Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand

INTRODUCTION: Regenerative peripheral nerve interfaces (RPNIs) are biological constructs which amplify neural signals and have shown long-term stability in rat models. Real-time control of a neuroprosthesis in rat models has not yet been demonstrated. The purpose of this study was to: a) design and...

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Autores principales: Frost, Christopher M., Ursu, Daniel C., Flattery, Shane M., Nedic, Andrej, Hassett, Cheryl A., Moon, Jana D., Buchanan, Patrick J., Brent Gillespie, R., Kung, Theodore A., Kemp, Stephen W. P., Cederna, Paul S., Urbanchek, Melanie G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245539/
https://www.ncbi.nlm.nih.gov/pubmed/30458876
http://dx.doi.org/10.1186/s12984-018-0452-1
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author Frost, Christopher M.
Ursu, Daniel C.
Flattery, Shane M.
Nedic, Andrej
Hassett, Cheryl A.
Moon, Jana D.
Buchanan, Patrick J.
Brent Gillespie, R.
Kung, Theodore A.
Kemp, Stephen W. P.
Cederna, Paul S.
Urbanchek, Melanie G.
author_facet Frost, Christopher M.
Ursu, Daniel C.
Flattery, Shane M.
Nedic, Andrej
Hassett, Cheryl A.
Moon, Jana D.
Buchanan, Patrick J.
Brent Gillespie, R.
Kung, Theodore A.
Kemp, Stephen W. P.
Cederna, Paul S.
Urbanchek, Melanie G.
author_sort Frost, Christopher M.
collection PubMed
description INTRODUCTION: Regenerative peripheral nerve interfaces (RPNIs) are biological constructs which amplify neural signals and have shown long-term stability in rat models. Real-time control of a neuroprosthesis in rat models has not yet been demonstrated. The purpose of this study was to: a) design and validate a system for translating electromyography (EMG) signals from an RPNI in a rat model into real-time control of a neuroprosthetic hand, and; b) use the system to demonstrate RPNI proportional neuroprosthesis control. METHODS: Animals were randomly assigned to three experimental groups: (1) Control; (2) Denervated, and; (3) RPNI. In the RPNI group, the extensor digitorum longus (EDL) muscle was dissected free, denervated, transferred to the lateral thigh and neurotized with the residual end of the transected common peroneal nerve. Rats received tactile stimuli to the hind-limb via monofilaments, and electrodes were used to record EMG. Signals were filtered, rectified and integrated using a moving sample window. Processed EMG signals (iEMG) from RPNIs were validated against Control and Denervated group outputs. RESULTS: Voluntary reflexive rat movements produced signaling that activated the prosthesis in both the Control and RPNI groups, but produced no activation in the Denervated group. Signal-to-Noise ratio between hind-limb movement and resting iEMG was 3.55 for Controls and 3.81 for RPNIs. Both Control and RPNI groups exhibited a logarithmic iEMG increase with increased monofilament pressure, allowing graded prosthetic hand speed control (R(2) = 0.758 and R(2) = 0.802, respectively). CONCLUSION: EMG signals were successfully acquired from RPNIs and translated into real-time neuroprosthetic control. Signal contamination from muscles adjacent to the RPNI was minimal. RPNI constructs provided reliable proportional prosthetic hand control. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-018-0452-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-62455392018-11-26 Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand Frost, Christopher M. Ursu, Daniel C. Flattery, Shane M. Nedic, Andrej Hassett, Cheryl A. Moon, Jana D. Buchanan, Patrick J. Brent Gillespie, R. Kung, Theodore A. Kemp, Stephen W. P. Cederna, Paul S. Urbanchek, Melanie G. J Neuroeng Rehabil Research INTRODUCTION: Regenerative peripheral nerve interfaces (RPNIs) are biological constructs which amplify neural signals and have shown long-term stability in rat models. Real-time control of a neuroprosthesis in rat models has not yet been demonstrated. The purpose of this study was to: a) design and validate a system for translating electromyography (EMG) signals from an RPNI in a rat model into real-time control of a neuroprosthetic hand, and; b) use the system to demonstrate RPNI proportional neuroprosthesis control. METHODS: Animals were randomly assigned to three experimental groups: (1) Control; (2) Denervated, and; (3) RPNI. In the RPNI group, the extensor digitorum longus (EDL) muscle was dissected free, denervated, transferred to the lateral thigh and neurotized with the residual end of the transected common peroneal nerve. Rats received tactile stimuli to the hind-limb via monofilaments, and electrodes were used to record EMG. Signals were filtered, rectified and integrated using a moving sample window. Processed EMG signals (iEMG) from RPNIs were validated against Control and Denervated group outputs. RESULTS: Voluntary reflexive rat movements produced signaling that activated the prosthesis in both the Control and RPNI groups, but produced no activation in the Denervated group. Signal-to-Noise ratio between hind-limb movement and resting iEMG was 3.55 for Controls and 3.81 for RPNIs. Both Control and RPNI groups exhibited a logarithmic iEMG increase with increased monofilament pressure, allowing graded prosthetic hand speed control (R(2) = 0.758 and R(2) = 0.802, respectively). CONCLUSION: EMG signals were successfully acquired from RPNIs and translated into real-time neuroprosthetic control. Signal contamination from muscles adjacent to the RPNI was minimal. RPNI constructs provided reliable proportional prosthetic hand control. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12984-018-0452-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-20 /pmc/articles/PMC6245539/ /pubmed/30458876 http://dx.doi.org/10.1186/s12984-018-0452-1 Text en © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Frost, Christopher M.
Ursu, Daniel C.
Flattery, Shane M.
Nedic, Andrej
Hassett, Cheryl A.
Moon, Jana D.
Buchanan, Patrick J.
Brent Gillespie, R.
Kung, Theodore A.
Kemp, Stephen W. P.
Cederna, Paul S.
Urbanchek, Melanie G.
Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title_full Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title_fullStr Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title_full_unstemmed Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title_short Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
title_sort regenerative peripheral nerve interfaces for real-time, proportional control of a neuroprosthetic hand
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245539/
https://www.ncbi.nlm.nih.gov/pubmed/30458876
http://dx.doi.org/10.1186/s12984-018-0452-1
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