High-performance neural prosthetic control along nstructed paths

Neural prostheses are becoming increasingly feasible as assistive technologies for paralyzed patients. A major goal is to provide control of a prosthesis rivaling the natural arm in speed, accuracy, and flexibility. Here, we demonstrate high-performance cursor control by training a monkey to move a cursor in a 2D virtual reality environment using neural activity recorded in primary motor cortex. On a standard center-out task with 8 possible targets, the subject maintained a success rate greater than 95% over many hundreds of trials, on par with previous reports. We introduced the more challenging task of moving the cursor along instructed paths with zero, one, and two inflections. Over several weeks, the subject´s performance with double-inflection paths reached a stable level of greater than 55% success with movement times approaching those of the natural arm. Our instructed trajectory task provides a new standard for quantification of prosthesis performance: since the subject´s intended movement is known (i.e. the instructed path), we can compute the root mean-square-error (RMSE) between the decoded and intended cursor position throughout the reach. We found that, while success rate tended to increase with training, the RMSE among successful trials remained largely unchanged, consistent with the all-or-none reward scheme. In sum, this work demonstrates the utility of instructed paths for i) pushing the limits of the subject´s control and ii) rigorously quantifying the accuracy of cursor movements, both of which are critical for increasing the clinical viability of neural prosthetic systems.