A High Performance Neurally-Controlled Cursor Positioning System

Prior work has shown that neural activity from the primate brain can maneuver a computer cursor to specified visual targets. This cursor movement can take over a second, longer than the time for an arm reach to the same location. We asked if this acquisition time could be reduced, thereby increasing the number of targets that could be hit per second. We implemented a system that positions a prosthetic cursor at discrete locations, based on pre-movement neural activity in rhesus monkeys. Using a delayed center-out reaching task with several different target layouts, neural activity was simultaneously recorded from an electrode array implanted in the dorsal pre-motor cortex. We designed a target prediction algorithm based on maximum-likelihood models (using Gaussian or Poisson distributions) to decode the upcoming reach target in real-time. During cursor trials, the algorithm predicted the most likely reach target using 50-275 ms of delay activity starting at least 150 ms after target onset If the target prediction was correct, a cursor was positioned and the monkey received a reward. The performance of the system was evaluated based on the accuracy of decoded targets and speed at which targets were decoded, both of which were consolidated with an information theoretic analysis. The maximum average sustained rate of target acquisition was 43 targets per second obtained with a 2 target layout and 50 ms of delay activity. The maximum information transfer rate calculated for the system was 6.5 bps obtained with an 8 target layout and 100 ms of delay activity