An optimal wavelet function based on wavelet denoising for multifunction myoelectric control

The aim of this study was to investigate and select the wavelet function that is optimum to denoise the surface electromyography (sEMG) signal for multifunction myoelectric control. Wavelet denoising algorithm has been used to find the optimal wavelet function for removing white Gaussian noise (WGN) at various signal-to-noise ratios (SNRs) from sEMG signals. A total of 53 wavelet functions were used in evaluation of the denoised performance. The wavelets are Daubechies, Symlets, Coiflet, BiorSplines, ReverseBior, and Discrete Meyer. Universal thresholding method has been used to estimate threshold value. Soft, hard, hyperbolic, and garrote thresholding are applied. Evaluations of the performance of these algorithms are mean squared error (MSE). The results show that the best wavelet functions for denoising are the first order of Daubechies, BioSplines, and ReverseBior wavelets (db1, bior1.1, rbio1.1). Various families can be used except the third order of decomposition of BiorSplines (bior3.1, bior3.3, bior3.5, bior3.7, bior3.9) and Discrete Meyer (dmey) are not recommended to use in wavelet denoising of sEMG signal. In addition, performance of soft thresholding is better than the others modified thresholding.