Date of Award
Daniel Felix Ritchie School of Engineering and Computer Science
Rahmat A. Shoureshi, Ph.D.
Adaptive control, Electroencephalography, Electromyography, Near-infrared spectroscopy, Neural network, Prosthetic limb control
Developing a non-invasive direct brain control of artificial limbs is both challenging and desirable. Such a sensory and control system, if successful, will have a profound impact on the disabled. In this dissertation, we present the design and development of a non-invasive, hybrid sensory system, which uses near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain activity with simultaneous electromyography (EMG) to provide feedback data in a healthy limb. Through the combination of these sensory techniques, we have successfully trained a control system capable of mapping brain activity onto muscle actuation. The design of a control algorithm capable of automatic reconfiguration to account for changing sensor conditions, selection of an appropriate pre-trained network based on input characteristics, and adaptation to adjust output based on the user's activity are investigated. The selection of an appropriate algorithm and its initial performance using our sensory system are presented and discussed.
The sensory and control system are designed for application in artificial limb control for persons who have undergone amputation of an upper-extremity. Actuation of the elbow and wrist are the primary focus of the study, with the intent to expand to forearm torsion and hand grasping in subsequent studies. During the course of the investigation, the additional function of treating phantom limb pain was incorporated into the design, which has also lead to increased sensor resolution requirements.
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Received from ProQuest
Aasted, Christopher, "Hybrid Sensing and Adaptive Control for Direct Brain Actuation of Artificial Limbs" (2011). Electronic Theses and Dissertations. 741.
Biomedical engineering, Engineering