通过调整潜在动态来稳定大脑与计算机的接口
Brianna M Karpowicz1, Yahia H Ali1, Lahiru N Wimalasena1
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Nature communications
|May 19, 2025
概括
这项研究介绍了非线性多重对齐与动力学 (NoMAD),这是稳定大脑-计算机接口的新方法. 在长时间内,NoMAD可以提高解码精度和稳定性,而不需要经常重新校准.
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
- 机器学习 机器学习
背景情况:
- 皮层内脑-计算机接口 (iBCI) 恢复运动功能,但患有解码不稳定性.
- 由于神经接口退化,当前的iBCI需要经常重新校准.
- 现有的iBCI稳定无监督方法无法解释潜在的神经动态.
研究的目的:
- 为iBCI开发一种稳定的解码方法,能够解释神经动态.
- 改善iBCI的长期性能,减少iBCI的重新校准需求.
- 为了利用潜在的多重结构和动态来实现强大的脑机接口控制.
主要方法:
- 开发了非线性多重对齐与动力学 (NoMAD) 平台.
- 利用循环神经网络模型来捕捉神经动态.
- 采用无监督分布对齐,将非静止的神经数据映射到一致的动态.
- 将NoMAD应用于执行运动任务的子的运动皮层数据.
主要成果:
- 通过NoMAD,从iBCI数据中实现了准确的行为解码.
- 在数周到数月的时间里,表现出无与伦比的解码稳定性.
- 消除了监督重新校准的需要.
- 展示了将动态纳入无监督iBCI稳定中的有效性.
结论:
- 在稳定的iBCI解码中,NoMAD提供了显著的进步.
- 对神经动态的考虑对长期iBCI性能至关重要.
- 这种无监督的方法有望恢复患者的运动功能.
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