Current commercially available motorized replacement limbs rely on the activation of remaining muscle tissue or a different portion of the body to operate joints in the prosthetic, which requires training to use and may never feel natural. Research to improve artificial limb technology is focused on using implants to monitor electrical activity in the nervous system or rerouting nerve endings to healthy muscle tissue, both of which require a medical procedure to be performed. This paper presents results of research which has been focused on the feasibility of using a non-invasive prosthetic control system that utilizes a hybrid of feedforward and feedback sensors. This research examines the correlation between brain activity across the primary motor cortex and muscle activity during upper body limb movement. The hybrid sensory system is composed of an optical imager for the detection of localized brain activities, electroencephalography (EEG) and electromyography (EMG) sensors. This paper will present design of the sensory system, the proposed control architecture, and human subject results. The improved accuracy of the brain intention determination algorithm as well as integration with a learning control loop will be presented and discussed.

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