论文 · 单臂试验
人运动皮层中对意图运动的准备期编码及其对脑机接口的意义
Preparatory encoding of intended movement in the human motor cortex and implications for brain-computer interfaces
作者:Mattia Rigotti-Thompson, Samuel R Nason-Tomaszewski, Payton H Bechefsky, Alexander Acosta, Nick V Hahn, Donald T Avansino, Brice A Richards, Claire Nicolas, Yahia H Ali, Jaimie M Henderson, Leigh R Hochberg, Nicholas AuYong 等 13 人
Curr Biol · 2026年8月11日 · Rigotti-Thompson 等 13 位作者
不需要生物学背景,多打比方
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摘要Abstract
Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated that this preparatory activity is also informative of initial and target directions of curved movement trajectories and encodes, to a weaker extent, either movement distance or speed. Finally, we present an online control paradigm that leverages preparatory activity to predict movements toward intended directions in advance, yielding rapid, user-initiated control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high-performance BCI applications.
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