Improving neural prosthetic system performance by combining plan and peri-movement activity.
While most neural prosthetic systems to date estimate arm movements based solely on the activity prior to reaching movements during a delay period (plan activity) or solely on the activity during reaching movements (peri-movement activity), we show that decode classification can be improved by 56% and 71% respectively by using both types of activity together. We recorded from the pre-motor cortex of a rhesus monkey performing a delayed-reach task to one of seven targets. We found that taking into account the time-varying structure in peri-movement activity further improved performance by 15%, while doing the same for plan activity did not improve performance. We also found low correlations in activity between pairs of simultaneously-recorded units and across time periods within a given trial condition. These results show that decode performance can be significantly improved by combining information from the plan and peri-movement periods, and that there is nearly no loss in performance when assuming independence between units and across tune periods within a given trial condition.
Yu
Byron M
BM
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Ryu
Stephen I
SI
Santhanam
Gopal
G
Churchland
Mark M
MM
Shenoy
Krishna V
KV