Neural Engineering Research Laboratory, Center for Biomedical Engineering, University of Campinas, Campinas, Brazil.
Department of Bioengineering, Federal University of Pernambuco, Recife, Brazil.
J Appl Physiol (1985). 2021 Aug 1;131(2):808-820. doi: 10.1152/japplphysiol.01041.2020. Epub 2021 Jul 8.
Cross talk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing cross talk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of cross talk. Three experiments were conducted. Participants (total 25) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the cross talk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of cross talk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant cross-talk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Cross talk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that cross talk is not reduced with high-pass temporal filtering. Conversely, spatial filtering reduced the cross-talk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce cross talk. This paper presents a new method for the identification and quantification of cross talk at the motor unit level and clarifies the influence of cross talk on EMG interpretation for muscles with different anatomy. We proposed a new method for the identification and quantification of cross talk at the motor unit level. We show that surface EMG cross talk can lead to physiological misinterpretations of EMG signals such as overestimations in the muscle activity and intermuscular correlation. Cross talk had little influence on the EMG power spectrum, which indicates that conventional temporal filtering cannot minimize cross talk. Spatial filter (single and double differential) effectively reduces but not abolish cross talk.
串扰是表面肌电图（EMG）信号解释的一个重要误差源。在这里，我们旨在通过识别单个运动单位动作电位来对三组协同肌肉的串扰进行特征描述。此外，我们还探讨了 EMG 信号的空间滤波（单差和双差）是否会影响串扰的程度。进行了三项实验。参与者（共 25 人）以数字肌肉和膝关节伸展肌的 10%最大随意收缩（MVC）和跖屈肌的 30% MVC 进行等长收缩。记录高密度表面 EMG 信号并将其分解为运动单位尖峰序列。对于每个肌肉，我们量化了相邻肌肉引起的串扰以及附近肌肉活动的污染程度。我们还估计了串扰对 EMG 功率谱和肌肉间相关性的影响。在单极记录模式下，大多数运动单位（80%）向相邻肌肉 EMG 产生显著的串扰信号，但随着空间滤波（单差和双差分别为 50%和 42%），这种比例降低。串扰导致肌肉间相关性的高估，并对 EMG 功率谱产生较小影响，这表明串扰并未随高通时间滤波而减少。相反，空间滤波降低了串扰幅度和肌肉间相关性的高估，证实了它是一种有效且简单的技术，可以减少串扰。本文提出了一种新的方法，用于在运动单位水平上识别和量化串扰，并阐明了串扰对具有不同解剖结构的肌肉 EMG 解释的影响。我们提出了一种新的方法，用于在运动单位水平上识别和量化串扰。我们表明，表面肌电图串扰可能导致对 EMG 信号的生理误解，例如肌肉活动和肌肉间相关性的高估。串扰对 EMG 功率谱的影响很小，这表明传统的时间滤波不能最小化串扰。空间滤波器（单差和双差）有效地降低但不能消除串扰。
