A robust measure of probability density function of various noises in electromyography (EMG) signal acquisition

Abstract

Statistical methods for estimating a probability density function (PDF) of surface electromyography (EMG) signals during upper-limb motions have been investigated in previous studies to select the suitable feature extraction methods for multifunction myoelectric control systems. While these methods have achieved a good performance in estimating PDF of EMG signals from different motions and muscles, no prior studies have evaluated the performance of these methods to estimate the PDF of noises in EMG signal acquisition. The utility of these methods consisting of bicoherence, kurtosis, negentropy, and L-kurtosis, was investigated in estimating the PDF of five different noise types: the single and many spurious background spikes, white Gaussian noise, motion artifact, and power line interference. The results show that the L-kurtosis can identify the PDF of all studied noises in EMG signal acquisition correctly. In contrast, other estimating methods are inaccuracy in at least one noise type.