A Scale Mixture-Based Stochastic Model of Surface EMG Signals with Variable Variances

Akira Furui, Hideaki Hayashi, Toshio Tsuji

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general model of the distribution of EMG signals that can explain both non-Gaussian and Gaussian distributions within a unified scheme. Methods: In this paper, we describe the formulation of a non-Gaussian EMG model based on a scale mixture distribution. In the model, an EMG signal at a certain time follows a Gaussian distribution, and its variance is handled as a random variable that follows an inverse gamma distribution. Accordingly, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances. The EMG variance distribution is estimated via marginal likelihood maximization. Results: Experiments involving nine participants revealed that the proposed model provides a better fit to recorded EMG signals than conventional EMG models. It was also shown that variance distribution parameters may reflect underlying motor unit activity. Conclusion: This study proposed a scale mixture distribution-based stochastic EMG model capable of representing changes in non-Gaussianity associated with muscle activity. A series of experiments demonstrated the validity of the model and highlighted the relationship between the variance distribution and muscle force. Significance: The proposed model helps to clarify conventional wisdom regarding the probability distribution of surface EMG signals within a unified scheme.

Original languageEnglish
Article number8627996
Pages (from-to)2780-2788
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume66
Issue number10
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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