González-Cueto José A, Erim Zeynep
Department of Electrical and Computer Engineering, Dalhousie University, Halifax, Canada.
IEEE Trans Biomed Eng. 2005 Nov;52(11):1846-50. doi: 10.1109/TBME.2005.856279.
The contribution of motor unit action potential trains (MUAPT) of distinct motor units (MU) to the crosscorrelation function between myoelectric signals (MES) recorded at the skin surface is studied. In specific, the significance of the correlation between the firing activity of concurrently active MUs (which results in cross-terms in the overall correlation function) is compared to the representation obtained using the contributions of single MUs at each recording site (auto-terms). A model for the generation of surface MUAPs is combined with the generation of MU firing statistics in order to obtain surface MUAPTs. MU firing statistics are simulated to incorporate MU synchronization levels reported in the literature. Alternatively, experimental firing statistics are fed to the model generating the MUAPTs. The contribution of individual MU pairs to the global myoelectric signal correlation function is assessed. Results indicate that the cross-terms from different MUs decrease steadily contributing very little to the overall correlation for record lengths as short as 30 s. Thus, the error expected when computing the crosscorrelation function between two channels of MES as the superposition of the auto-terms contributed by single MUs (i.e., ignoring the cross-terms from different MUs) is shown to be very small.
研究了不同运动单位（MU）的运动单位动作电位序列（MUAPT）对在皮肤表面记录的肌电信号（MES）之间互相关函数的贡献。具体而言，将同时激活的运动单位（其导致总相关函数中的交叉项）的放电活动之间的相关性的重要性与使用每个记录部位单个运动单位的贡献（自项）获得的表示进行比较。将表面运动单位动作电位（MUAP）的生成模型与运动单位放电统计的生成相结合，以获得表面MUAPT。模拟运动单位放电统计以纳入文献中报道的运动单位同步水平。或者，将实验放电统计数据输入生成MUAPT的模型。评估单个运动单位对之间对全局肌电信号相关函数的贡献。结果表明，来自不同运动单位的交叉项稳步下降，对于短至30秒的记录长度，对总相关性的贡献非常小。因此，当将两个MES通道之间的互相关函数计算为单个运动单位贡献的自项的叠加（即忽略来自不同运动单位的交叉项）时，预期的误差非常小。
