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从大脑信号中解码多关节的手动,通过学习基于协同的神经元组

Huaqin Sun^1,2, Zhengyi Wang^1,2, Yu Qi^1,2

¹Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China.

Patterns (New York, N.Y.)

|December 2, 2025

概括

脑机接口现在可以通过理解运动协同作用来解码复杂的手动. 这种新的SynergyNet框架解码时空参数,显著改善了用于运动功能重建的神经解码.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
机器人技术机器人技术机器人技术

背景情况:

大脑-计算机接口 (BCI) 显示出恢复运动功能的前景.
解读复杂的自然动作,如手动,仍然是一个重大挑战.
现有的方法往往解码单个关节运动,忽视了潜在的运动协同作用.

研究的目的:

探索运动协同作用在复杂的手部运动中的作用.
开发一种新的神经解码框架,以改进手部运动重建.
调查解码电机协同效应是否比传统的联合级解码具有优势.

主要方法:

复杂的手动的分解成原始的动力 (协同作用).
学习这些协同作用的联合神经运动表现.
开发和应用SynergyNet框架来解码时空参数.
将SynergyNet与手动解码的基准方法进行比较.

主要成果:

证明复杂的手动可以通过运动协同作用来表示.
展示了通过时空参数招募运动协同作用有助于复杂的运动控制.
在手动解码准确度方面,SynergyNet显著超过了现有的基准方法.
拟议的方法在神经解码中提供了高的解释性.

结论:

运动协同作用对复杂的手动运动至关重要,并且可以有效地解码.
解码运动协同作用的时空参数提供了一个比关节级解码更有效的方法.
协同网络框架为运动控制中的脑计算机接口应用提供了一个强大而易于解释的工具.

关键词:

大脑-计算机接口接口解码手部运动的解码.手和手的协同作用.运动皮层的运动皮层.神经解码的神经解码

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