🤖 AI Summary
This study addresses the challenge of decoding whole-body motor behaviors driven by cortical neural activity in primates under natural, unconstrained conditions. To this end, we developed a neuro-behavioral recording and modeling framework for freely moving macaques, integrating large-scale epidural electrocorticographic signals with multi-view synchronized motion capture data. We introduced an autoregressive encoder-decoder model to reconstruct full-body movements and learn compact behavioral priors. This work represents the first demonstration of high-fidelity, unconstrained whole-body movement decoding in primates directly from intracranial neural signals, transcending the limitations of traditional constrained-task paradigms and establishing the feasibility of accurately reconstructing complex naturalistic behaviors from cortical activity alone.
📝 Abstract
Understanding how cortical activity represents natural whole-body behaviors in primates remains challenging. Limited by the diversity of movements and inaccessibility of large-scale neural representation of whole-body kinematics, previous motor decoding studies focused on constrained tasks and limited limb movements. Here, we present a neural-behavioral recording and modeling framework for freely moving monkeys, combining large-scale epidural cortical signals from distributed sensory- and motor-related areas with synchronized multi-view motion capture through a custom-made data collection platform. We reconstructed whole-body monkey kinematics and learned a compact behavior prior using an autoregressive encoder-decoder model. Conditioned on neural signals, the model decoded accurate and realistic whole-body movement without explicit physical constraints. Our results provide a novel proof-of-concept approach for decoding natural whole-body movements in primates using large-scale intracranial neural activity.