肌肉协同作用中的抑制性成分由来自电肌图数据的修正潜变量模型构成因子
IEEE journal of biomedical and health informatics
|October 9, 2024
概括
修正后潜变量模型 (RLVM) 提取了非负矩阵因子化 (NMF) 遗漏的负肌肉协同组件. 这种新方法揭示了运动神经科学中的抑制神经控制,推进了肌肉协同分析.
科学领域:
- 运动神经科学 运动神经科学
- 计算神经科学是一种神经科学.
- 生物物理学的生物物理.
背景情况:
- 非负矩阵因子化 (NMF) 是从电肌图信号 (EMG) 来进行肌肉协同分析的标准.
- NMF无法识别负面肌肉成分,可能缺少抑制性神经控制.
- 阻断性脊柱内神经元在运动控制中起作用,但不会被NMF捕获.
研究的目的:
- 引入和验证用于肌肉协同提取的修正后隐性变量模型 (RLVM).
- 为了克服NMF在识别负面肌肉组件方面的局限性.
- 探索抑制性脊髓内神经元在肌肉协同作用中的作用.
主要方法:
- 使用RLVM的自编码神经网络架构.
- 在模拟和实验EMG数据集上,RLVM与NMF进行了比较.
- 应用RLVM对子的上肢EMG和脊柱前运动内部神经元记录在抓取过程中.
主要成果:
- 在模拟中,RLVM在识别肌肉协同子空间方面表现出卓越的表现.
- 在实验数据中,RLVM确定了NMF中缺少的小负肌肉成分 (NegCps).
- RLVM的NegCps与脊柱前运动内神经元的抑制性肌肉场相关.
结论:
- RLVM是一种可行的方法,可以从EMG中提取潜在的抑制肌肉协同功能的组件.
- 这些发现表明,RLVM可以揭示以前无法检测到的神经控制机制.
- 这有助于我们更好地理解涉及抑制途径的运动控制.
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