Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

TLDR

Reaching and grasping in primates depend on coordinated neural activity in large frontoparietal ensembles. The study demonstrates that primates can learn to reach and grasp virtual objects by controlling a robot arm via a closed‑loop brain–machine interface that decodes multiple motor parameters from frontoparietal neuronal activity. The BMIc uses multiple mathematical models to extract hand position, velocity, gripping force, and EMGs of arm muscles from recorded neuronal ensembles. High BMIc accuracy required large neuronal ensembles, and continuous operation improved model predictions and behavioral performance; monkeys achieved robot reach‑and‑grasp movements without arm movement, with learning accompanied by functional reorganization across multiple cortical areas.

Abstract

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.