Sherif Elbasiouny (Advisor), Caroline Cao (Committee Member), Subhashini Ganapathy (Committee Member)
Master of Science in Biomedical Engineering (MSBME)
The overarching goal of this project is to develop novel neural motor decoders for prosthetic control. EMG decoders measure the activity from an intact but non-target muscle. Neural motor decoders transform the signal measured from the severed motor axons of the target muscle. A multi-scale, highly-realistic computer model of a spinal motor pool was developed (Allen & Elbasiouny, 2018) to serve as a computational platform for decoder development and testing. A firing rate-based algorithm was developed to transform the aggregate discharge of the motor pool into a command signal to control the simulated prosthetic MuJoCo hand. This algorithm was informed by cellular neurophysiology knowledge of how motor neurons are activated by synaptic inputs to generate action potentials. Our results show that this neural motor decoder is fast (i.e., decoding time < 10 ms), reliable (i.e., accurate decoding of inputs varying in waveform, magnitude, and speed), and robust (i.e., accurate decoding of varying activation schemes) in controlling the prosthesis. Additionally, this decoder was successful in automatically adapting, in real-time, to dynamic changes in the synaptic input signals and decoding its magnitude and rate of activation; thus, minimizing the need for frequent daily calibrations by the amputee.
Department or Program
Department of Biomedical, Industrial & Human Factors Engineering
Year Degree Awarded
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.