Extracting Neural Drives from Surface EMG: A Generative Model and Simulation Studies
Extracting Neural Drives from Surface EMG: A Generative Model and Simulation Studies
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DOI：
10.1109/iembs.2007.4353423
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
Ning Jiang;P. Parker;K. Englehart
中科院分区：
文献类型：
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作者：
Ning Jiang;P. Parker;K. Englehart
提出了一种表面肌电信号的生成模型。该模型是建立在这样的假设上，即协同肌肉中的运动单元共享来自脊髓水平的神经驱动，其对应于自然运动的不同自由度（DOF）的激活，并且嵌入在表面EMG内。一个人工神经网络（ANN）的开发，同时提取这些驱动器从多通道表面肌电信号。该技术的直接应用将是向能够同时控制多个DOF的假体装置提供控制信号。它在脊髓损伤和其他神经肌肉疾病的诊断和康复方面也有潜在的应用。
A generative model for the surface EMG is presented. The model is built on the assumption that motor units in synergistic muscle share neural drives from spinal level, which correspond to the activation of different degrees of freedom (DOF) of natural movements, and are embedded within surface EMG. An artificial neural network (ANN) is developed to extract these drives simultaneously from the multi-channel surface EMG. A direct application of this technique would be providing control signals to prosthetic devices that are capable of simultaneous control of multiple DOF. It also has potential applications in the diagnosis and rehabilitation of spinal cord injuries and other neuromuscular disorders.