The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach.

中文翻译：

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使用主激活提取的肌肉协同作用：增强鲁棒性和可解释性。

肌肉协同作用理论已被广泛用于评估人类运动过程中中枢神经系统（CNS）的模块化组织。应用于表面肌电图（sEMG）信号的预处理方法会影响肌肉协同作用的提取。该贡献的目的是评估通过使用创新的预处理方法获得的肌肉协同作用提取的改进。我们根据肌肉协同作用的数量，受试者内部的一致性，健壮性和结果的可解释性来评估改进。本文提出的预处理方法是基于从原始sEMG信号中提取肌肉主要激活（严格完成特定生物力学任务所必需的肌肉激活），然后仅使用主要激活来获得肌肉协同作用。本文呈现的结果表明，这种新颖方法在提取肌肉协同作用方面的应用相对于标准预处理方法，为CNS的模块化组织提供了更可靠，更易解释的描述。