Decoding Methods for Locomotor Brain-Machine Interfaces

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2015

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Abstract

Cortical representations of rhythmic and discrete movements are analyzed and used to create a novel neural decoding algorithm for brain-machine interfaces. This algorithm is then implemented to decode both cyclic movements and reach-and-hold movements in awake behaving rhesus macaques using their cortical activity alone. Finally, a healthy macaque wears and controls a lower body exoskeleton using the developed BMIas a proof of concept of a brain-controlled neuroprosthetic device for locomotion.

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Dissertation

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Zhuang, Katie (2015). Decoding Methods for Locomotor Brain-Machine Interfaces. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/11349.

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