A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees

Abstract

Peripheral nerves provide a promising source of motor control signals for neuroprosthetic devices. Unfortunately, the clinical utility of current peripheral nerve interfaces is limited by signal amplitude and stability. Here, we showed that the regenerative peripheral nerve interface (RPNI) serves as a biologically stable bioamplifier of efferent motor action potentials with long-term stability in upper limb amputees. Ultrasound assessments of RPNIs revealed prominent contractions during phantom finger flexion, confirming functional reinnervation of the RPNIs in two patients. The RPNIs in two additional patients produced electromyography signals with large signal-to-noise ratios. Using these RPNI signals, subjects successfully controlled a hand prosthesis in real-time up to 300 days without control algorithm recalibration. RPNIs show potential in enhancing prosthesis control for people with upper limb loss.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Figures

Fig. 1.. Fine wire electrode insertion and anatomical illustrations of RPNI surgical creation.

(A) Illustration of multiple RPNIs created for each available nerve. Percutaneous bipolar hook electrodes were embedded into the RPNI muscle belly of P1 during acute sessions. (B) P1 who had a proximal transradial amputation had nine RPNIs created: four for the median nerve, three for the ulnar nerve, and two for the radial nerve. (C) Illustration of the creation of P2’s RPNIs at the glenohumeral amputation level. P2 had eight RPNIs created: two each for the median, ulnar, and radial nerves, and one each for the musculocutaneous and axillary nerves. (D and E) Both P3 and P4 had amputations at the distal transradial level. P3 had three RPNIs implanted, one on each of the median, ulnar, and radial nerves, whereas P4 had four RPNIs implanted, one on each of the median and radial nerve and two on the ulnar nerve.

Fig. 2.. RPNI sonograms, motor map, and electrophysiology.

(A) P1’s median and ulnar RPNI sonograms captured 19 months after RPNI surgery. Encircled areas on the sonogram show which region of the median or ulnar RPNIs contracted during cued finger movements. (B) P2’s sonogram of two RPNIs captured 8 months after RPNI surgery and motor map of active areas. (C) P1’s EMG signals (blue) recorded from median RPNI 1 after cued thumb IP joint movement (red dashed line), and EMG signals (blue) recorded from ulnar RPNI 1 and RPNI 2 after cued small finger PIP/DIP movement (red dashed line).

Fig. 3.. RPNI mean absolute value signals during six different finger movements.

(A) P3’s median and ulnar RPNI MAV signals during thumb carpometacarpal (CMC)/metacarpophalangeal (MCP) joint flexions, thumb interphalangeal (IP) joint flexion, index finger MCP/proximal interphalangeal (PIP), small finger MCP/PIP joint flexions, hand abduction, and hand adduction movements. (B) P4’s median RPNI, ulnar RPNI 1, and ulnar RPNI 2 MAV signals during thumb CMC/MCP joint flexions, thumb IP joint flexion, index finger MCP/PIP joint flexions, small finger MCP/PIP joint flexions, finger abduction, and finger adduction movements.

Fig. 4.. Real-time classification of finger movements.

(A and B) P3 and P4’s discrete control of thumb MCP joint (opposition), thumb IP joint (flexion), small finger, adduction, and rest for P3, and ring finger, thumb IP joint, small finger, abduction, and rest for P4. The fastest motion selection times are shown for each posture. (C and D) Offline confusion matrix of the postures used in (A) and (B), respectively. The y axis represents the true posture, whereas the x axis represents the predicted posture. The color map indicates the accuracy (%) of the classifier’s prediction.

Fig. 5.. Real-time continuous control of the virtual and physical prosthesis.

(A and B) Examples of P3 and P4’s real-time predicted trajectories (blue) for one DOF thumb IP joint movement across multiple days using a one-time calibrated decoding algorithm. The y axis represents the percentage of flexion, 0% equals finger fully extended, 50% equals finger at rest, and 100% equals finger fully flexed. Each maize rectangle indicates the target was successfully acquired, whereas red rectangles indicate unsuccessful trials. The width of the rectangle represents how long the virtual target was displayed, whereas the height represents the size of the virtual target. (C and D) Single motor units extracted across days from the median RPNIs of P3 and P4. Blue and shaded trace represents mean and SD of extracted units. (E and F) Example of predicted trajectories during real-time two DOF continuous decoding of thumb CMC/MCP/IP joint movements in virtual space. (G) An equivalent target hitting task in physical space using the LUKE arm (Deka).