基于自组织映射神经网络和卷积核补偿的多通道表面肌电信号的盲源分离方法

The Blind Source Separation Method Based on Self-organizing Map Neural Network and Convolution Kernel Compensation for Multi-channel sEMG Signals

本文基于已有的卷积核补偿(CKC)方法,提出了一种新的信号分解方法。该方法与自组织映射神经网络相结合,首先找出一个在某一时刻具有发放活动的脉冲序列,其次对这个脉冲序列的一些较大值所对应的时刻利用自组织映射神经网络进行分类,然后利用分类后的时刻所对应的测量信号的值求出最终的一个信号源的发放序列。通过随机混合矩阵合成产生的仿真信号进行测试,表明所提出的方法是有效的。

A new method based on convolution kernel compensation（CKC）for decomposing multi-channel surface electromyogram（sEMG）signals is proposed in this paper.Unsupervised learning and clustering function of self-organizing map（SOM）neural network are employed in this method.An initial innervations pulse train（IPT）is firstly estimated,some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network.Then the final IPT can be obtained from the observations corresponding to these time instants.In this paper,the proposed method was tested on the simulated signal,the influence of signal to noise ratio（SNR）,the number of groups clustered by SOM and the number of highest peaks selected from the initial pulse train on the number of reconstructed sources and the pulse accuracy were studied,and the results show that the proposed approach is effective in decomposing multi-channel sEMG signals.