Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition.
Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition.
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渐进式 FastICA 剥离和卷积核补偿展示了高密度表面 EMG 分解的高度一致性
DOI：
10.1155/2016/3489540
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发表时间：
2016
影响因子：
3.1
通讯作者：
Zhou P
中科院分区：
文献类型：
--
作者：
Chen M;Holobar A;Zhang X;Zhou P
肌电分解是研究运动单位可塑性的重要方法。各种信号处理技术已被开发用于高密度表面肌电信号的分解，其中卷积核补偿（CKC）已获得了广泛验证的高分解率。最近，一个渐进的FastICA剥离（PFP）框架也已开发用于高密度表面EMG分解。在这项研究中，CKC和PFP方法独立应用于分解相同的高密度表面肌电信号。在91个试验的64通道表面肌电信号记录从第一背侧骨间（FDI）肌肉的9个神经功能完整的科目，有1477个运动单位，从这两种方法，包括969个常见的运动单位。平均每项试验识别出10.6 ± 4.3个常见运动单位，放电瞬间匹配率为97.85 ± 1.85%。CKC和PFP处理中常见运动单位的高度一致性为两种方法的分解准确性提供了支持性证据。从每种方法中获得的不同电机单元也表明，两种方法的组合可能具有进一步提高分解产率的潜力。
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.
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影响因子：
5.4
作者：
Holobar, Ales;Zazula, Damjan
通讯作者：
Zazula, Damjan
影响因子：
4.6
作者：
Li, Xiaoyan;Suresh, Aneesha;Rymer, William Zev
通讯作者：
Rymer, William Zev
一种基于 FastICA 的高密度表面肌电分解新框架。
DOI：
10.1109/tnsre.2015.2412038
发表时间：
2016-01
期刊：
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Chen M;Zhou P
通讯作者：
Zhou P
影响因子：
3.2
作者：
Holobar, A;Zazula, D
通讯作者：
Zazula, D
影响因子：
2.5
作者：
Stashuk, D
通讯作者：
Stashuk, D