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

arXiv: https://arxiv.org/abs/1912.04580 · HTML: https://mlsanity.com/paper/1912.04580/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models](https://mlsanity.com/paper/2107.09853/) (2021)
- [Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces](https://mlsanity.com/paper/2403.20234/) (2024)
- [Evaluating Classifier Confidence for Surface EMG Pattern Recognition](https://mlsanity.com/paper/2304.05898/) (2023)

## Abstract (arXiv)

Objective: 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.