In order to eliminate the noise mixed in surface electromyography ( sEMG) , the paper presents a new sEMG de-noising method based on ensemble empirical mode decomposition (EEMD) and second generation wavelet transform. Firstly, the white noise-added sEMG signals are decomposed by the empirical mode decomposition ( EMD). Secondly, the high-frequency Intrinsic Mode Function (IMF) components are denoised by the second generation wavelet threshold method. Finally,the high frequency IMF components processed and low frequency IMF components are reconstructed to get the denoised signal. The experimental results show that the method combines the advantages of second generation wavelet and EEMD, which can better eliminate noise, retain the useful signal as much as possible,and has a higher signal-to-noise ratio.%为了更好地消除混杂在表面肌电信号(sEMG)中的噪声,提出了一种基于总体平均经验模式分解(EEMD)和二代小波变换的sEMG消噪新方法.首先对信号加入白噪声处理后进行经验模态分解(EMD),然后对高频的内蕴模式函数(IMF)分量进行二代小波阈值消噪处理,最后把处理后的高频IMF分量与低频IMF分量进行叠加,重构后的信号即为去噪信号.实验结果表明,该方法融合了二代小波与EEMD的优点,能更好的消除噪声,最大限度的保留有用信号,并具有更高的信噪比.
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