Decoding of plan and peri-movement neural signals in prosthetic systems

In this paper we introduce a theoretical framework for improved processing of peri-movement neural activity for neurally controlled prosthetic systems through maximum likelihood sequence estimation. This framework further suggests a computational method for integrating plan and peri-movement neural activity. We show that combining plan activity, usually associated with target specification, with peri-movement neural activity yields more accurate estimates of the trajectory of an arm movement. The effectiveness of the method is demonstrated in simulation. Performance as a function of the specific number of plan and peri-movement neurons, as well as other system and design parameters is analyzed. The algorithm presented is also compared against previous, sample-based approaches, specifically a "point-process" filter for plan activity and a standard linear filter framework in the peri-movement regime.