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

Abstract 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) des...

Full description

Bibliographic Details
Main Authors: Christopher M. Frost, Daniel C. Ursu, Shane M. Flattery, Andrej Nedic, Cheryl A. Hassett, Jana D. Moon, Patrick J. Buchanan, R. Brent Gillespie, Theodore A. Kung, Stephen W. P. Kemp, Paul S. Cederna, Melanie G. Urbanchek
Format: Article
Language:English
Published: BMC 2018-11-01
Series:Journal of NeuroEngineering and Rehabilitation
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12984-018-0452-1
_version_ 1819274154531094528
author Christopher M. Frost
Daniel C. Ursu
Shane M. Flattery
Andrej Nedic
Cheryl A. Hassett
Jana D. Moon
Patrick J. Buchanan
R. Brent Gillespie
Theodore A. Kung
Stephen W. P. Kemp
Paul S. Cederna
Melanie G. Urbanchek
author_facet Christopher M. Frost
Daniel C. Ursu
Shane M. Flattery
Andrej Nedic
Cheryl A. Hassett
Jana D. Moon
Patrick J. Buchanan
R. Brent Gillespie
Theodore A. Kung
Stephen W. P. Kemp
Paul S. Cederna
Melanie G. Urbanchek
author_sort Christopher M. Frost
collection DOAJ
description Abstract 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 (R2 = 0.758 and R2 = 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.
first_indexed 2024-12-23T23:03:55Z
format Article
id doaj.art-71a217e4fe2c46b29b5fa064b6287b55
institution Directory Open Access Journal
issn 1743-0003
language English
last_indexed 2024-12-23T23:03:55Z
publishDate 2018-11-01
publisher BMC
record_format Article
series Journal of NeuroEngineering and Rehabilitation
spelling doaj.art-71a217e4fe2c46b29b5fa064b6287b552022-12-21T17:26:52ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032018-11-011511910.1186/s12984-018-0452-1Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic handChristopher M. Frost0Daniel C. Ursu1Shane M. Flattery2Andrej Nedic3Cheryl A. Hassett4Jana D. Moon5Patrick J. Buchanan6R. Brent Gillespie7Theodore A. Kung8Stephen W. P. Kemp9Paul S. Cederna10Melanie G. Urbanchek11University of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryVassar CollegeUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Mechanical EngineeringUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryUniversity of Michigan Department of Surgery, Section of Plastic SurgeryAbstract 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 (R2 = 0.758 and R2 = 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.http://link.springer.com/article/10.1186/s12984-018-0452-1Peripheral nerve InterfaceProstheticsRegenerative medicineAmputees
spellingShingle Christopher M. Frost
Daniel C. Ursu
Shane M. Flattery
Andrej Nedic
Cheryl A. Hassett
Jana D. Moon
Patrick J. Buchanan
R. Brent Gillespie
Theodore A. Kung
Stephen W. P. Kemp
Paul S. Cederna
Melanie G. Urbanchek
Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
Journal of NeuroEngineering and Rehabilitation
Peripheral nerve Interface
Prosthetics
Regenerative medicine
Amputees
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 Peripheral nerve Interface
Prosthetics
Regenerative medicine
Amputees
url http://link.springer.com/article/10.1186/s12984-018-0452-1
work_keys_str_mv AT christophermfrost regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT danielcursu regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT shanemflattery regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT andrejnedic regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT cherylahassett regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT janadmoon regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT patrickjbuchanan regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT rbrentgillespie regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT theodoreakung regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT stephenwpkemp regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT paulscederna regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand
AT melaniegurbanchek regenerativeperipheralnerveinterfacesforrealtimeproportionalcontrolofaneuroprosthetichand