在家运动状态分类使用完全可植入的皮质-基底神经接口神经接口
Rithvik Ramesh1, Hamid Fekri Azgomi1, Kenneth H Louie1
1Department of Neurological Surgery, University of California, San Francisco, CA, USA.
Science advances
|February 13, 2026
概括
研究人员在日常活动中使用植入式设备的脑信号来解码行走. 这一突破使得帕金森病患者适应性神经调节的实时运动状态分类成为可能.
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
- 神经调节是一种神经调节.
背景情况:
- 从神经信号解读人类运动通常需要基于实验室的机器学习和短期数据.
- 这种方法限制了对自然行为和临床应用的理解,例如闭环神经调节.
研究的目的:
- 为了展示第一个在人身上,在家中使用完全可植入的双向神经刺激器进行行走的分类.
- 建立现实世界的神经解码管道和个性化自适应神经调节的框架.
主要方法:
- 在四名帕金森病患者中记录了慢性运动皮质和球活动,这些患者在无人监督的日常活动中 (超过80小时).
- 同步的神经数据与可穿戴的运动数据.
- 确定了个性化的步态光谱生物标志物,并验证了它们的性能.
- 利用神经刺激器的嵌入式线性分辨分类器进行实时运动状态分类.
主要成果:
- 成功识别了步态的预测性个性化光谱生物标志物.
- 使用这些生物标志物和神经刺激器的分类器进行实时运动状态分类.
- 在封闭循环刺激的设备级限制范围内验证了分类性能.
结论:
- 建立了一个新的管道,用于真实世界的神经解码人类运动.
- 开发了一个可扩展的框架,用于个性化的自适应神经调节.
- 扩大了植入式脑电脑接口的翻译潜力,以实现自然主义行为.
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