Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach

Rory O'Keeffe, Seyed Yahya Shirazi, Sarmad Mehrdad, Tyler Crosby, Aaron M. Johnson, S. Farokh Atashzar

Research output: Contribution to journalArticlepeer-review


Objective: Objective evaluation of physiological responses using non-invasive methods for the assessment of vocal performance and voice disorders has attracted great interest. This paper, for the first time, aims to implement and evaluate perilaryngeal-cranial functional muscle networks. The study investigates the variations in topographical characteristics of the network and the corresponding ability to differentiate vocal tasks. Method: Twelve surface electromyography (sEMG) signals were collected bilaterally from six perilaryngeal and cranial muscles. Data were collected from eight subjects (four females) without a known history of voice disorders. The proposed muscle network is composed of pairwise coherence between sEMG recordings. The network metrics include (a) network degree and (b) weighted clustering coefficient (WCC). Results: The varied phonation tasks showed the median degree, and WCC of the muscle network ascend monotonically, with a high effect size ($|r_{rb}|\sim 0.5$). Pitch glide, singing, and speech tasks were significantly distinguishable using degree and WCC ($|r_{rb}|\sim 0.8$). Also, pitch glide had the highest degree and WCC among all tasks (degree$>0.7$, WCC$>0.75$). In comparison, classic spectrotemporal measures showed far less effectiveness (max $|r_{rb}|=0.12$) in differentiating the vocal tasks. Conclusion: Perilaryngeal-cranial functional muscle network was proposed in this paper. The study showed that the functional muscle network could robustly differentiate the vocal tasks while the classic assessment of muscle activation fails to differentiate. Significance: For the first time, we demonstrate the power of a perilaryngeal-cranial muscle network as a neurophysiological window to vocal performance. In addition, the study also discovers tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.

Original languageEnglish (US)
Pages (from-to)3678-3688
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number12
StatePublished - Dec 1 2022


  • Voice disorder
  • intermuscular coherence
  • neurophysiology
  • surface electromyography

ASJC Scopus subject areas

  • Biomedical Engineering


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