PROCESSING IN A NEUROPROSTHETIC MODEL by

Brain-computer interfaces (BCI) are being developed which provide volitional control of a motor prosthesis to patients who suffer from motor-debilitating diseases and conditions. Neuroprosthetic devices are controlled by activity from the patient’s brain and bypass damaged parts of the spinal cord or peripheral nervous system. Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a BCI. No systematic analysis of the need for spike sorting has been carried out, and little is known about the effects of spike sorting error on the ability of a BCI to decode intended motor commands. We developed a theoretical framework and computational methodology to examine the effects of spike processing on the information available to a BCI decoder. Shannon information theory was applied to simulated neural data. Results demon-strated that reported amounts of spike sorting error reduce mutual information (MI) significantly in single-unit spike trains. We also studied how much information i