Journal of NeuroEngineering and Rehabilitation (Nov 2018)

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

  • 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

DOI
https://doi.org/10.1186/s12984-018-0452-1
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 9

Abstract

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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.

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