| Summary: | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 78-79). === The closed-loop Brain-Machine Interface (BMI) has long been a dream for clinicians and neuroscience researchers alike - that is, the ability to extract meaningful information from the brain, perform computation on this information, and selectively perturb neural dynamics in the brain for therapeutic benefit to the patient. Such systems have immediate application to treatment of paralysis, epilepsy and the amputated, and the potential for treatment of higher order cognitive dysfunction. Despite the promise of the BMI concept, the technology for bidirectional communication with the brain at sufficiently large scale to be truly therapeutically useful is lacking. Current state-of-the-art neuromodulation systems deliver open loop, 16-channel patterned electrical stimulation incapable of precisely targeting small numbers of neurons. Large-scale neural recording systems are limited to 16-128 electrodes, at the cost of several thousand dollars per channel. The ability to record from the awake behaving animal - let alone precisely modulate neural network dynamics in closed-loop fashion- presents a substantial challenge today. === In this thesis, I present decoupled design solutions for three critical subcomponents of the closed-loop BMI - (i) a highly miniature, wirelessly powered and wirelessly controlled implantable optogenetic neuromodulation system capable of selective neural network control with single neural subtype- and millisecond-timescale precision, (ii) a prototype, highly parallel and scalable bio-potential recording system for simultaneous monitoring of many thousands of electrodes, and (iii) a space- and energy-efficient battery charger for biomedical applications. In aggregate, these systems overcome many of the fundamental architectural problems seen in the research and clinical environment today, potentially enabling a new class of neuromodulation system capable of treatment of higher-order cognitive dysfunction. In the research setting, these systems may be scaled to enable whole-brain recording, potentially yielding insights into large-scale neural network dynamics underlying disease and cognition. === by Christian T. Wentz. === M.Eng.
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