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为了消除肌电信号中的噪声,提出了一种基于相邻尺度积系数的硬阈值滤波方法.通过对采集的肌电信号进行小波分解,并对肌电信号的各层噪声方差进行估计,构造一种基于相邻尺度积系数的硬阈值函数,实现了真实信号与噪声的分离.根据保留下的小波系数进行重构,得到滤波后的信号.实验表明该方法能有效消除噪声,且基本保留了真实信号的边缘特征,为提高基于肌电信号的手部动作识别率提供了技术手段.
In order to eliminate the noise in EMG signal, a hard threshold filtering method based on adjacent scale product coefficients is proposed. By analyzing the EMG signals collected by wavelet transform and estimating the variance of noise in each layer of EMG signal, A hard threshold function based on the adjacent scale product coefficients realizes the separation of the real signal from the noise, and reconstructs the signal according to the preserved wavelet coefficients to obtain the filtered signal. Experiments show that this method can effectively eliminate the noise and basically retain The edge features of the real signal provide technical means to improve the hand movement recognition rate based on EMG signal.