A Novel Muscle Innervation Zone Estimation Method Using Monopolar High Density Surface Electromyography

Chengjun Huang, Maoqi Chen, Yingchun Zhang, Sheng Li, Cliff S. Klein, Ping Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study presents a novel method to estimate a muscle's innervation zone (IZ) location from monopolar high density surface electromyography (EMG) signals. Based on the fact that 2nd principal component coefficients derived from principal component analysis (PCA) are linearly related with the time delay of different channels, the channels located near the IZ should have the shortest time delays. Accordingly, we applied a novel method to estimate a muscle's IZ based on PCA. The performance of the developed method was evaluated by both simulation and experimental approaches. The method based on 2nd principal component of monopolar high density surface EMG achieved a comparable performance to cross-correlation analysis of bipolar signals when noise was simulated to be independently distributed across all channels. However, in simulated conditions of specific channel contamination, the PCA based method achieved superior performance than the cross-correlation method. Experimental high density surface EMG was recorded from the biceps brachii of 9 healthy subjects during maximum voluntary contractions. The PCA and cross-correlation based methods achieved high agreement, with a difference in IZ location of 0.47 ± 0.4 IED (inter-electrode distance = 8 mm). The results indicate that analysis of 2nd principal component coefficients provides a useful approach for IZ estimation using monopolar high density surface EMG.

Original languageEnglish (US)
Pages (from-to)22-30
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
StatePublished - 2023


  • High density surface electromyography (EMG)
  • innervation zone (IZ)
  • principal component analysis (PCA)
  • the 2nd principal component

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation


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