Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University Aalborg, Denmark.
Pain Clinic Center for Anesthesiology, Emergency and Intensive Care Medicine, University Hospital Göttingen Göttingen, Germany.
Front Hum Neurosci. 2014 May 23;8:335. doi: 10.3389/fnhum.2014.00335. eCollection 2014.
Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.
运动可以通过肌电图（EMG）信号的分解来研究，例如使用非负矩阵分解（NMF）。这种方法是一种方便简洁的肌肉活动表示形式，作为分布式在运动模块中，在特定步态阶段激活。为了应用 NMF，EMG 信号可以作为单个试验进行分析，也可以作为平均 EMG 或串联 EMG（数据结构）进行分析。本研究的目的是研究数据结构对提取的运动模块的影响。12 名健康男性在跑步机上以自己的速度行走，同时记录 60s 来自 10 个下肢肌肉的表面肌电信号。通过 NMF 从 40 个步周期中分别提取代表协同肌肉激活相对权重的运动模块（EMGSNG），从 2、3、5、10、20 和 40 个连续周期的平均值（EMGAVR）中提取，以及从相同组连续周期的串联中提取（EMGCNC）。无论使用哪种数据结构，都需要五个运动模块才能重建原始 EMG 数据集（重建质量>90%）。然而，与 EMGAVR 相比，EMGCNC 与稍低的重建质量相关。从不同的数据结构中提取的大多数运动模块都是相似的（相似度>0.85）。然而，当使用来自 EMGAVR 的肌肉权重重建 40 步长的 EMGCNC 数据集时，重建质量较低（重建质量40%）。另一方面，当使用来自 EMGCNC 的权重重建这段长时间的运动时，提供了更高的质量，尤其是使用 20 个串联的步骤（重建质量80%）。尽管 EMGSNG 和 EMGAVR 显示出对短信号间隔的更高重建质量，但这些数据结构没有考虑到步长之间的可变性。本研究的结果为运动中肌电协同肌肉激活提取的方法学方面提供了实用指南。
