Bimodal brain-machine interface for motor control of robotic prosthetic

We are working on mapping multi-channel neural spike data, recordedfrom multiple cortical areas ofan owl monkey, to corresponding 3d monkey arm positions. In earlier work on this mapping task, we observed that continuous function approximotors (such as artificial neural networks) have diflculty in jointly estimating 36 arm positions for two distinct cases namely, when the monkey’s arm is stationary and when if is moving. Therefore, we propose a multiple-model approach thatfirst classifies neural spike data into two classes, corresponding to two states of the moneky’s arm: (1) stationary and (2) moving. Then, the output of this classijier is used (IS a gating mechanism for subsequent continuous models, with one modelper class. In this paper. we first motivote and discuss our approach. Next, we present encouraging results for the class$er stage, based on hidden Markov models (HMMsJ, and also for the entire bimodal mapping system. Finally, we conclude with a discussion ofthe results and suggestfuture avenues of research.