Motor Control and Sensory Feedback Enhance Prosthesis Embodiment and Reduce Phantom Pain After Long-Term Hand Amputation

We quantified prosthesis embodiment and phantom pain reduction associated with motor control and sensory feedback from a prosthetic hand in one human with a long-term transradial amputation. Microelectrode arrays were implanted in the residual median and ulnar arm nerves and intramuscular electromyography recording leads were implanted in residual limb muscles to enable sensory feedback and motor control. Objective measures (proprioceptive drift) and subjective measures (survey answers) were used to assess prosthesis embodiment. For both measures, there was a significant level of embodiment of the physical prosthetic limb after open-loop motor control of the prosthesis (i.e., without sensory feedback), open-loop sensation from the prosthesis (i.e., without motor control), and closed-loop control of the prosthesis (i.e., motor control with sensory feedback). There was also a statistically significant reduction in reported phantom pain after experimental sessions that included open-loop nerve microstimulation, open-loop prosthesis motor control, or closed-loop prosthesis motor control. The closed-loop condition provided no additional significant improvements in phantom pain reduction or prosthesis embodiment relative to the open-loop sensory condition or the open-loop motor condition. This study represents the first long-term (14-month), systematic report of phantom pain reduction and prosthesis embodiment in a human amputee across a variety of prosthesis use cases

Individual hand movement detection and classification using peripheral nerve signals

This paper investigates whether the movement intent of an amputee can be detected and classified in real-time as the individual moved his/her phantom hand. We present a method to detect movement intent using neural signals from the peripheral nervous system (PNS). In addition, we classify eight types of individual hand movements using 300 ms signal segments beginning with our detected starting time. Classification is performed by applying linear discriminant analysis (LDA) on different kind of features. We compared the classification results using segments started with the detected starting time and the starting time of the command given to a subject as neural signals were recorded. The average accuracies were 73.5% in the former case and 59.4% in the latter