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Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand
Open accessAbstract
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. 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. 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). 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.
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Intuitive motor control can be attained via advanced techniques such as targeted muscle reinnervation, regenerative peripheral nerve interfaces, or myoelectric pattern recognition.
“Intuitive motor control can be attained via the use of the advanced techniques such as targeted muscle reinnervation (TMR), regenerative peripheral nerve interfaces (RPNIs) or the use of myoelectric pattern recognition [19–21].”
From Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review (2020) · cites this paper as [19]
The Results report that RPNI signals enabled graded control of a prosthetic hand in rats, and the Introduction and Discussion describe TMR and myoelectric pattern-recognition approaches. But the Discussion limits this experiment to one RPNI and single-axis actuation in a rat model; it does not establish that these techniques attain intuitive motor control in human users. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRegenerative peripheral nerve interfaces are surgically constructed from muscle grafts obtained from expendable skeletal muscle in the residual limb or from a distant site, and are neurotized by terminal branches of residual nerves.
“Another option is to utilize RPNIs, which are surgically constructed from muscle grafts obtained from expendable skeletal muscle in the residual limb or from a distant site. These muscle grafts are neurotized by the terminal branches of the residual nerves (Fig. 2b), which are surgically dissected into its fascicles before the neurotization [19].”
From Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review (2020) · cites this paper as [19]
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References 45 · Cited by 68
Research works printed but not linked: 1
- Guide for the Care and Use of Laboratory Animals. Nat'l Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals. 8th ed. Washington (DC): Guide for the Care and Use of Laboratory Animals; 2011.
Metadata
- Authors
- Frost, C. M., Ursu, D. C., Flattery, S. M., Nedic, A., Hassett, C. A., Moon, J. D., Buchanan, P. J., Brent Gillespie, R., Kung, T. A., Kemp, S. W. P., Cederna, P. S., & Urbanchek, M. G.
- Affiliations
- University of Michigan (US)
- Vassar College (US)
- Corresponding author
- Ursu, Daniel C.
- ORCID
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2018-11-20
- Volume
- 15
- Issue
- 1
- Pages
- 108-108
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC6245539
- OpenAlex
- W2901220113
- Cited by
- 68
- References
- 45
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 9 · 2023: 6 · 2022: 15 · 2021: 6 · 2020: 12 · 2019: 2
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Defense Advanced Research Projects Agency (N66001-11-C-4190)
- National Institute of General Medical Sciences (T32 GM008616)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- regenerative peripheral nerve interface, electromyography, proportional control, rat model, denervation, peroneal nerve, extensor digitorum longus, signal-to-noise ratio, peripheral nerve regeneration
- MeSH terms
- Animals, Artificial Limbs, Electromyography, Hindlimb, Male, Movement, Nerve Regeneration, Peripheral Nerves, Signal Processing, Computer-Assisted, Muscle, Skeletal, Rats
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