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

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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 (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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          A neural interface provides long-term stable natural touch perception.

          Daniel W Tan, Matthew A Schiefer, Michael Keith … (2014)
          Touch perception on the fingers and hand is essential for fine motor control, contributes to our sense of self, allows for effective communication, and aids in our fundamental perception of the world. Despite increasingly sophisticated mechatronics, prosthetic devices still do not directly convey sensation back to their wearers. We show that implanted peripheral nerve interfaces in two human subjects with upper limb amputation provided stable, natural touch sensation in their hands for more than 1 year. Electrical stimulation using implanted peripheral nerve cuff electrodes that did not penetrate the nerve produced touch perceptions at many locations on the phantom hand with repeatable, stable responses in the two subjects for 16 and 24 months. Patterned stimulation intensity produced a sensation that the subjects described as natural and without "tingling," or paresthesia. Different patterns produced different types of sensory perception at the same location on the phantom hand. The two subjects reported tactile perceptions they described as natural tapping, constant pressure, light moving touch, and vibration. Changing average stimulation intensity controlled the size of the percept area; changing stimulation frequency controlled sensation strength. Artificial touch sensation improved the subjects' ability to control grasping strength of the prosthesis and enabled them to better manipulate delicate objects. Thus, electrical stimulation through peripheral nerve electrodes produced long-term sensory restoration after limb loss.
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            Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

            Todd Kuiken, Guanglin Li, Blair A. Lock … (2009)
            Improving the function of prosthetic arms remains a challenge, because access to the neural-control information for the arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce electromyogram (EMG) signals on the surface of the skin that can be measured and used to control prosthetic arms. To assess the performance of patients with upper-limb amputation who had undergone TMR surgery, using a pattern-recognition algorithm to decode EMG signals and control prosthetic-arm motions. Study conducted between January 2007 and January 2008 at the Rehabilitation Institute of Chicago among 5 patients with shoulder-disarticulation or transhumeral amputations who underwent TMR surgery between February 2002 and October 2006 and 5 control participants without amputation. Surface EMG signals were recorded from all participants and decoded using a pattern-recognition algorithm. The decoding program controlled the movement of a virtual prosthetic arm. All participants were instructed to perform various arm movements, and their abilities to control the virtual prosthetic arm were measured. In addition, TMR patients used the same control system to operate advanced arm prosthesis prototypes. Performance metrics measured during virtual arm movements included motion selection time, motion completion time, and motion completion ("success") rate. The TMR patients were able to repeatedly perform 10 different elbow, wrist, and hand motions with the virtual prosthetic arm. For these patients, the mean motion selection and motion completion times for elbow and wrist movements were 0.22 seconds (SD, 0.06) and 1.29 seconds (SD, 0.15), respectively. These times were 0.06 seconds and 0.21 seconds longer than the mean times for control participants. For TMR patients, the mean motion selection and motion completion times for hand-grasp patterns were 0.38 seconds (SD, 0.12) and 1.54 seconds (SD, 0.27), respectively. These patients successfully completed a mean of 96.3% (SD, 3.8) of elbow and wrist movements and 86.9% (SD, 13.9) of hand movements within 5 seconds, compared with 100% (SD, 0) and 96.7% (SD, 4.7) completed by controls. Three of the patients were able to demonstrate the use of this control system in advanced prostheses, including motorized shoulders, elbows, wrists, and hands. These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.
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              A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems.

              Xavier Navarro, Thilo Krueger, Natalia Lago … (2005)
              Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.
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                Author and article information

                Contributors
                Christopher M. Frost: cfrost12@gmail.com
                Daniel C. Ursu:
                ORCID: http://orcid.org/0000-0002-6579-103X
                734-936-2817 , danursu@umich.edu
                Shane M. Flattery: sflatter@med.umich.edu
                Andrej Nedic: anedic12@gmail.com
                Cheryl A. Hassett: cherylhassett@gmail.com
                Jana D. Moon: fjana@med.umich.edu
                Patrick J. Buchanan: Patrick.Buchanan@surgery.ufl.edu
                R. Brent Gillespie: brentg@umich.edu
                Theodore A. Kung: thekung@med.umich.edu
                Stephen W. P. Kemp: swpkemp@med.umich.edu
                Paul S. Cederna: cederna@med.umich.edu
                Melanie G. Urbanchek: melurban@med.umich.edu
                Journal
                Journal ID (nlm-ta): J Neuroeng Rehabil
                Journal ID (iso-abbrev): J Neuroeng Rehabil
                Title: Journal of NeuroEngineering and Rehabilitation
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1743-0003
                Publication date (Electronic): 20 November 2018
                Publication date PMC-release: 20 November 2018
                Publication date Collection: 2018
                Volume: 15
                Electronic Location Identifier: 108
                Affiliations
                [1 ]ISNI 0000000086837370, GRID grid.214458.e, University of Michigan Department of Surgery, Section of Plastic Surgery, ; 570 MSRB II Level A, 1150 W. Medical Center Drive, Ann Arbor, MI 48109-5456 USA
                [2 ]ISNI 0000000086837370, GRID grid.214458.e, University of Michigan Department of Mechanical Engineering, ; Ann Arbor, MI USA
                [3 ]ISNI 0000 0001 2290 5183, GRID grid.267778.b, Vassar College, ; Poughkeepsie, NY USA
                [4 ]ISNI 0000000086837370, GRID grid.214458.e, Department of Biomedical Engineering, , University of Michigan, ; Ann Arbor, MI USA
                Author information
                http://orcid.org/0000-0002-6579-103X
                Article
                Publisher ID: 452
                DOI: 10.1186/s12984-018-0452-1
                PMC ID: 6245539
                PubMed ID: 30458876
                SO-VID: fe5d45f6-503d-4bdb-a022-4bb8f36c9650
                Copyright © © The Author(s). 2018
                License:

                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.

                History
                Date received : 25 May 2018
                Date accepted : 31 October 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000185, Defense Advanced Research Projects Agency;
                Award ID: N66001-11-C-4190
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32 GM008616
                Award Recipient :
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

                ScienceOpen disciplines: Neurosciences
                Keywords: peripheral nerve interface,prosthetics,regenerative medicine,amputees
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: peripheral nerve interface, prosthetics, regenerative medicine, amputees

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