December 1, 2025Open Access

Standardizing EMG Pipelines for Muscle Synergy Analysis: A Large-Scale Evaluation of Filtering, Normalization and Criteria

KZKunkun ZhaoNanjing Medical University YJYuanyuan JinSuzhou University of Science and Technology YFYizhou FengNanjing Medical University

Key Points

Muscle synergy analysis was improved by evaluating various filtering and normalization methods.
Results indicated reproducibility is enhanced by standardized EMG processing techniques across diverse parameters.
Surface electromyography findings provide insights into muscle performance and movement strategy assessments.
Robust guidelines were established to aid clinical application of muscle synergy evaluations and facilitate future study comparisons.

Abstract

Muscle synergies offer valuable insights into the movement strategies employed by the central nervous system and present a promising avenue for clinical applications. However, the field lacks a complete understanding of how surface electromyography processing parameters affect muscle synergy analysis, which in turn has hindered cross-study comparisons and the translation of experimental results to clinical contexts. To address the gap, this study presents a systematic evaluation of interactive effects of three key parameters on muscle synergy analysis, including nine cut-off frequencies of low-pass filters, five normalization methods, and five synergy extraction criteria, covering 225 unique combinations. Using a comprehensive running dataset of 135 subjects, this study examined variance accounted for (VAF) and correlation coefficient (R2) metrics, the number of synergies, and synergy structure consistency under different parameter settings. Synergy similarity was used as a quantitative measure of synergy stability across different parameter settings. The results demonstrated that cut-off frequencies, normalization methods, and criteria choices interactively influenced the outcomes. Notably, VAF consistently yielded higher values than R2, highlighting differences in how these metrics capture explained variance. Error VAF (EVAF) emerged as the most robust criterion for determining the number of synergies, especially when combined with normalization methods by maximum value (MAX), average value (AVE), or unit variance (UVA) and moderately high cut-off frequencies, which led to more stable synergy structures across conditions. Furthermore, the predefined threshold associated with each criterion markedly affected the estimated number of synergies. These findings provide structured guidelines for muscle synergy analysis, helping to standardize preprocessing and extraction parameters, improve reproducibility across studies, and enhance the clinical applicability of synergy-based assessments.

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