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Using two-dimensional spatial information in decomposition of surface EMG signals

机译:使用二维空间信息分解表面EMG信号

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Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential. (c) 2006 Published by Elsevier Ltd.
机译:最近,高密度表面EMG电极网格和多通道放大器可用于无创记录人体电机(MU)。我们提出了一种将表面EMG信号分解为MU触发模式的方法,从而我们专注于MU动作电位(MUAP)之间的二维空间差异的重要性。我们的方法以来自单个受试者的股外侧肌的高密度EMG数据为例。将双极和拉普拉斯空间滤波应用于单极原始信号。从该受试者的单次记录中,可以使用垂直于肌肉纤维方向的MUAP之间的空间差异来区分六个不同的同时活动的MU。经过尖峰触发平均后,获得了125通道的二维MUAP模板。模板匹配允许跟踪所有MU触发。通过使用与肌肉纤维方向平行或垂直的MUAP模板子集,可以测量空间信息的影响。垂直于肌肉纤维方向的一维空间信息的使用优于在纵向方向上的线性阵列电极的使用。然而,为了高精度地检测MU的点火事件,例如对于估计点火同步所需的,来自完整栅极的二维信息显得必不可少。 (c)2006年由Elsevier Ltd.发布。

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