用大脑信号还原猴子自然运动，突破传统实验限制。
Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys
- 结合脑电与多视角动作捕捉，构建自由运动记录框架。
- 模型仅凭神经信号就精准重建全身动作，无需物理约束。
- 适合神经工程、脑机接口研究者关注其跨模态建模思路。
理解灵长类大脑如何表征自然的全身行为仍具挑战性。受限于运动多样性及全身运动学大规模神经表征的不可及性，以往运动解码研究多集中于受控任务和有限肢体运动。本文提出一种针对自由活动猴子的神经-行为记录与建模框架，通过自研数据采集平台，同步获取来自分布于感觉-运动相关区域的大规模硬膜外皮层信号与多视角动作捕捉数据。我们重构了猴子全身运动学，并利用自回归编码器-解码器模型学习了一个紧凑的行为先验。在神经信号条件下，该模型可无显式物理约束地解码出准确且逼真的全身运动。结果为利用大规模颅内神经活动解码灵长类自然全身运动提供了新范式。
原文摘要 · Abstract (English)
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.
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