从表面EMG使用空间尖峰检测评估肌肉刺激的累积尖峰列车估计
IEEE journal of biomedical and health informatics
|August 29, 2023
概括
本研究引入了一种新的空间尖峰检测方法,用于从表面电肌图 (sEMG) 估计累积尖峰列车 (CST). 该技术提高了控制神经接口的精度,特别是在多度自由度的手腕运动中.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 信号处理 信号处理
背景情况:
- 从表面电肌图 (sEMG) 准确估计动力单元 (MU) 累积尖峰列车 (CST) 对于推进神经接口控制至关重要.
- 目前的CST估计方法缺乏复杂神经接口应用所需的精度,限制了它们的发展.
研究的目的:
- 开发一种简单而高效的方法,使用高密度sEMG的空间尖峰检测来识别CST.
- 提高CST估计的准确性,以提高神经接口中的性能.
主要方法:
- 采用空间滑窗方法来检测基于其空间传播特征的动力单元动作潜能尖峰.
- 该方法侧重于在当地区域内识别激活的MU的峰值,而不是针对单个MU.
- 验证涉及多度自由度 (DoF) 腕部运动实验,将拟议的方法与已建立的CST估计和MU分解技术进行比较.
主要成果:
- 与现有方法相比,拟议的方法在多DoF手腕扭矩估计方面表现出更高的准确性.
- 在估计和记录的力之间实现了高相关系数 (R = 0.96 ± 0.03) 和低的正常化根平均平方误差 (nRMSE = 10.1% ± 3.7%).
- 从拟议方法中获得的CST与通过MU分解获得的CST显示出强烈的相关性.
结论:
- 开发的空间尖峰检测方法为从sEMG信号中识别CST提供了显著的改进.
- 这种技术提供了更准确和可靠的驱动信号,为更有效的神经接口控制铺平了道路.
- 这些发现凸显了这种方法在神经假肢和人机接口的未来进步方面的潜力.
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