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Purpose <p>We evaluated two high-performing surface electromyography (sEMG) decomposition methods—k-means convolution kernel compensation (KmCKC) and SVD plus LMMSE (SL)—under challenging conditions, including high motor unit (MU) firing synchrony, short signal durations, elevated firing rates, and varying maximum voluntary contraction (MVC) levels. These findings provide valuable insights for researchers and clinicians.</p> Methods <p>Four categories of sEMG signals were analyzed using both simulated and experimental data: signals with varying MU firing synchrony (20%, 40%, 60%), short-duration signals (0.3–10&#xa0;s), signals with different average MU firing rates (up to 50&#xa0;Hz), and signals acquired at different MVC levels (10%, 30%, 50%). Decomposition performance was evaluated based on the number of identified MUs, precision, and pulse-to-noise ratio (PNR).</p> Results <p>SL consistently outperformed KmCKC across all conditions. At 20% MU firing synchrony, SL identified 11.3 MUs versus KmCKC’s 5.7. For short-duration signals (&lt; 1.3&#xa0;s), KmCKC failed to identify any MUs, while SL detected 3.3–6.3 MUs. At a 40&#xa0;Hz firing rate, SL decomposed 12.7 MUs compared to KmCKC’s 3. Across different MVC levels, SL maintained superior performance with precision exceeding 98.7%.</p> Conclusion <p>The SL method demonstrates superior decomposition performance compared to KmCKC, particularly for complex sEMG signals. SL is the preferred method for short-duration signals and those with high MU firing synchrony and firing rates.</p>

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Benchmarking sEMG Decomposition: A Comprehensive Assessment of KmCKC and SL Across Synchrony, Duration, MVC, and Firing Rate

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Yong Ning,
  • Zhenggang Han,
  • Mingchun Liu,
  • Jinbao He,
  • Honghai Liu,
  • Weiyi Huang,
  • Chuang Li,
  • Yanhong Fang,
  • Yongzhi Sun

摘要

Purpose

We evaluated two high-performing surface electromyography (sEMG) decomposition methods—k-means convolution kernel compensation (KmCKC) and SVD plus LMMSE (SL)—under challenging conditions, including high motor unit (MU) firing synchrony, short signal durations, elevated firing rates, and varying maximum voluntary contraction (MVC) levels. These findings provide valuable insights for researchers and clinicians.

Methods

Four categories of sEMG signals were analyzed using both simulated and experimental data: signals with varying MU firing synchrony (20%, 40%, 60%), short-duration signals (0.3–10 s), signals with different average MU firing rates (up to 50 Hz), and signals acquired at different MVC levels (10%, 30%, 50%). Decomposition performance was evaluated based on the number of identified MUs, precision, and pulse-to-noise ratio (PNR).

Results

SL consistently outperformed KmCKC across all conditions. At 20% MU firing synchrony, SL identified 11.3 MUs versus KmCKC’s 5.7. For short-duration signals (< 1.3 s), KmCKC failed to identify any MUs, while SL detected 3.3–6.3 MUs. At a 40 Hz firing rate, SL decomposed 12.7 MUs compared to KmCKC’s 3. Across different MVC levels, SL maintained superior performance with precision exceeding 98.7%.

Conclusion

The SL method demonstrates superior decomposition performance compared to KmCKC, particularly for complex sEMG signals. SL is the preferred method for short-duration signals and those with high MU firing synchrony and firing rates.