VMD-based denoising methods for surface electromyography signals

Since the noise is inevitably introduced in the measurement process of surface electromyographic (sEMG) signal, two novel methods for denoising based on Variational Mode Decomposition (VMD) method were proposed in this work. Before this, there is no literature on how VMD is applied to sEMG de-noise. The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow-band, and then wavelet soft thresholding (WST) method is applied on each VMF. This method is named as VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signal of biceps brachii was measured and analyzed. In this paper, three methods are used for quantitative evaluation of filtering performance: signal noise ratio (SNR), root mean square error (RMSE) and R-square. The proposed two methods (VMD-WST, VMD-SIT) are compared with Empirical Mode Decomposition (EMD) method and Wavelet method. The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effect were better than EMD method and Wavelet method. The VMD-SIT method has the best performance. This study provides a new way based on VMD method to eliminate the noise of sEMG signal, and it can be applied in the field of limb movement classification, disease diagnosis, human-machine interaction and so on.

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