Negro Francesco, Muceli Silvia, Castronovo Anna Margherita, Holobar Ales, Farina Dario
Institute of Neurorehabilitation Systems, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University of Göttingen, Göttingen, Germany.
J Neural Eng. 2016 Apr;13(2):026027. doi: 10.1088/1741-2560/13/2/026027. Epub 2016 Feb 29.
The study of motor unit behavior has been classically performed by selective recording systems of muscle electrical activity (EMG signals) and decomposition algorithms able to discriminate between individual motor unit action potentials from multi-unit signals. In this study, we provide a general framework for the decomposition of multi-channel intramuscular and surface EMG signals and we extensively validate this approach with experimental recordings.
First, we describe the conditions under which the assumptions of the convolutive blind separation model are satisfied. Second, we propose an approach of convolutive sphering of the observations followed by an iterative extraction of the sources. This approach is then validated using intramuscular signals recorded by novel multi-channel thin-film electrodes on the Abductor Digiti Minimi of the hand and Tibilias Anterior muscles, as well as on high-density surface EMG signals recorded by electrode grids on the First Dorsal Interosseous muscle. The validation was based on the comparison with the gold standard of manual decomposition (for intramuscular recordings) and on the two-source method (for comparison of intramuscular and surface EMG recordings) for the three human muscles and contraction forces of up to 90% MVC.
The average number of common sources identified for the validation was 14 ± 7 (averaged across all trials and subjects and all comparisons), with a rate of agreement in their discharge timings of 92.8 ± 3.2%. The average Decomposability Index, calculated on the automatic decomposed signals, was 16.0 ± 2.2 (7.3-44.1). For comparison, the same index calculated on the manual decomposed signals was 15.0 ± 3.0 (6.3-76.6).
These results show that the method provides a solid framework for the decomposition of multi-channel invasive and non-invasive EMG signals that allows the study of the behavior of a large number of concurrently active motor units.
运动单位行为的研究传统上是通过肌肉电活动（肌电图信号）的选择性记录系统以及能够从多单位信号中区分单个运动单位动作电位的分解算法来进行的。在本研究中，我们提供了一个用于多通道肌内和表面肌电图信号分解的通用框架，并通过实验记录对该方法进行了广泛验证。
首先，我们描述了卷积盲分离模型假设得到满足的条件。其次，我们提出了一种对观测值进行卷积球化然后迭代提取源的方法。然后使用新型多通道薄膜电极在手小指展肌和胫骨前肌上记录的肌内信号，以及在第一背侧骨间肌上通过电极网格记录的高密度表面肌电图信号对该方法进行验证。验证基于与手动分解的金标准（用于肌内记录）以及双源方法（用于比较肌内和表面肌电图记录）进行比较，涉及三块人体肌肉且收缩力高达90%最大自主收缩。
验证中识别出的共同源的平均数量为14±7（在所有试验、受试者和所有比较中平均），其放电时间的一致率为92.8±3.2%。根据自动分解信号计算的平均可分解性指数为16.0±2.2（7.3 - 44.1）。相比之下，根据手动分解信号计算的相同指数为15.0±3.0（6.3 - 76.6）。
这些结果表明，该方法为多通道侵入性和非侵入性肌电图信号的分解提供了一个坚实的框架，有助于研究大量同时活跃的运动单位的行为。
