Controlling a Neuroprosthetic Arm: Real Time Estimation and Prediction
The development of neuroprosthetic devices promises to allow previously immobile patients to control the movement of an external device using only their brains' electrical activity. Prediction algorithms used to control such devices rely on models relating the firing rates of a population of neurons to intended movement variables such as direction. However, since no data on real arm movement will be available prior to use of the prosthetic, and recent research has shown that neurons may change their firing patterns in response to visual feedback, an algorithm is needed that in addition to predicting movement can also perform real time estimation of the model. This article proposes statistical methods for performing these related tasks and demonstrates the methods' effectiveness using data taken from a series of experiments using a rhesus monkey.