Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys

Understanding how cortical activity represents natural whole-body behavior in primates remains challenging. Limited by diversity of movements and inaccessible of large-scale neural feature programming whole-body kinematics, previous motor decoding studies rely on constrained tasks and limited limb movements. Here, we present a neural-behavioral recording and modeling framework for freely moving monkeys, combining epidural cortical signals from distributed sensorimotor areas with synchronized multi-view motion capture through a data custom collection platform. We reconstruct whole-body monkey kinematics and learn a compact motion prior using an autoregressive encoder-decoder model. Conditioned on epidural signals, the model decodes more accurate and realistic full-body movements without explicit physical constraints. Our results provides a novel proof-of-concept approach for decoding natural whole-body movements in primates using large-scale intracranial neural activity.