Abstract
Control of prostheses using cortical signals is based on three elements:
chronic microelectrode arrays, extraction algorithms, and prosthetic
effectors. Arrays of microelectrodes are permanently implanted in
cerebral cortex. These arrays must record populations of single-
and multiunit activity indefinitely. Information containing position
and velocity correlates of animate movement needs to be extracted
continuously in real time from the recorded activity. Prosthetic
arms, the current effectors used in this work, need to have the agility
and configuration of natural arms. Demonstrations using closed-loop
control show that subjects change their neural activity to improve
performance with these devices. Adaptive-learning algorithms that
capitalize on these improvements show that this technology has the
capability of restoring much of the arm movement lost with immobilizing
deficits.
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