A Laplacian-Gaussian Mixture Model for Surface EMG Signals from Upper Limbs

Existing literature suggests that the probability density function (pdf) of surface Electromyography (sEMG) signals follows either a Gaussian or Laplacian model. In this paper, a Laplacian-Gaussian mixture model is proposed for the EMG signals extracted from the upper limbs. The model is validated using both quantitative and qualitative perspectives. Specifically, for a benchmark dataset, the Kullback-Leibler (KL) divergence is computed between the proposed model and the histogram based empirical probability density function (mpdf). For a sample signal, a goodness of fit plot with R squared value and a visual comparison between the histogram based mpdf and the estimated pdf from the proposed model are presented. Moreover, the Expectation-Maximization (EM) algorithm is derived for the estimation of the parameters of the proposed mixture model. The weight of the Laplacian component is computed for each of the signals from a benchmark dataset. It has been empirically determined that the Laplacian component has a major contribution to the mixture.