Dissertation Defense: Joseph Betthauser
Title: Robust Adaptive Strategies for Myographic Prosthesis Movement Decoding
Abstract: Improving the condition-tolerance, stability, response time, and dexterity of neural prosthesis control strategies are major clinical goals to aid amputees in achieving natural restorative upper-limb function. Currently, the dominant noninvasive neural source for prosthesis motor control is the skin-surface recorded electromyographic (EMG) signal. Decoding movement intentions from EMG is a challenging problem because this signal type is subject to a high degree of interference from noise and conditional influences. As a consequence, much of the movement intention information contained within the EMG signal has remained significantly under-utilized for the purposes of controlling robotic prostheses. We sought to overcome this information deficit through the use of adaptive strategies for machine learning, sparse representations, and signal processing to significantly improve myographic prosthesis control. This body of research represents the current state-of-the-art in condition-tolerant EMG movement classification (Chapter 3), stable and responsive EMG sequence decoding during movement transitions (Chapter 4), and positional regression to reliably control 7 wrist and finger degrees-of-freedom (Chapter 5). To our knowledge, the methods we describe in Chapter 5 elicit the most dexterous, biomimetic, and natural prosthesis control performance ever obtained from the surface EMG signal.