Variance distribution analysis of surface EMG signals based on marginal maximum likelihood estimation.

This paper describes the estimation and analysis of variance distribution of surface electromyogram (EMG) signals based on a stochastic EMG model. With the assumption that EMG signals at a certain time follow Gaussian distribution, their variance is handled as a random variable that follows inverse gamma distribution, and noise superimposed onto this variance can be expressed accordingly. The paper proposes variance distribution estimation based on marginal likelihood maximization of EMG signals. A simulation experiment using artificially generated signals to verify its accuracy indicated that the method can estimate variance distribution with high accuracy for a wide range of variance distribution shaping. Analysis of variance distribution using measured EMG signals revealed the relationship between muscle force and variance distribution involving signal-dependent noise.