Decoding the neural drive for finer and more intuitive control of a myoelectric robotic hand

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The loss of an upper limb is often caused by traumatic events, such as work-related injuries, road traffic accidents, and military casualties. Unlike other types of amputation, almost three quarters of upper limb referrals are young (less than 55 years) and otherwise healthy individuals. Despite decades of research, commercial active prostheses still use technology developed in the sixties, namely myoelectric control via superficial electrodes. Stated simply, they are controlled by electrical impulses recorded from the patient's residual forearm muscles using a small number of surface electrodes. Their functionality is constrained by the limited amount of voluntary information that can be extracted from the small number of surface electrodes adopted. As a result, even the most advanced of these prostheses only allow a small number of pre-defined simple grip shapes that the user can select from. Users of myoelectric prostheses often express a desire for improved functionality, wider range of grip shapes, and more intuitive proportional control. A main problem of today's prostheses is that the movements are decoded through classifiers (i.e. algorithms trained to recognize patterns in the electromyographic signal), which must be trained for the specific movement.Within this project, we conduct ambitious research into the development of novel decoding algorithms to make the control of myoelectric hand prostheses more natural, intuitive, and accurate. Our approach uses recently developed high-density surface electromyographic (hd-sEMG) arrays, which record from a high number of closely spaced electrodes, combined with the most advanced signal processing and neural decoding techniques.The use of hd-sEMG allows the extraction of more information by giving access to individual motor unit action potentials which can be used to reconstruct the neural drive, i.e. the train of electrical pulses (spikes) that encode the information on the motor task sent to the muscles.Access to these spike trains allows the use of a type of statistical models and algorithms, called "point processes", of which the principal investigator is an expert. These algorithms work by first trying to understand how the motor task is "encoded" in the spike spike trains and then reverting this process in order to infer the most likely motor task given the observed signal. They can be trained with arbitrary movements and have the potential to decode complex movements that were never observed during training. The ultimate goal is to have a controller that allows arbitrary movements rather than a set of pre-defined movements.Throughout the project, the principal investigator and the research assistant will benefit from collaborating with world experts in the field of robotic prosthetic hands and neural signal processing.