基于EEMD阈值处理的脑电信号降噪方法  

Means for EEG signal denoising based on EEMD threshold

作　　者：郭晓梅 朱晓军[1] GUO Xiao-mei;ZHU Xiao-jun(College of Computer Science and Technology,Taiyuan University of Technology,Jinzhong 030600,China)

机构地区：[1]太原理工大学计算机科学与技术学院,山西晋中030600

出　　处：《计算机工程与设计》2018年第11期3408-3414,3427,共8页Computer Engineering and Design

基　　金：山西省青年基金项目(2013021016-3)

摘　　要：为提升对非线性非平稳信号的消噪性能,在EEMD阈值消噪法的基础上结合平移不变(translation invariant,TI)算法,提出一种TI总体经验模态分解自适应阈值处理的EEG去噪方法。EEMD阈值消噪有效避免小波阈值法在小波基选择上存在的缺陷,平移不变算法思想的引入进一步抑制模态混叠现象的发生。以信噪比、均方根误差、皮尔逊相关系数和最大峰值误差作为定量分析标准,将所提方法与其它算法进行比较,仿真和真实信号的实验结果均表明,EEMD阈值法与平移不变算法的有效结合使消噪性能更优。To improve the denoising performance of nonlinear and non-stationary signals,EEMD threshold denoising method combined with the translation invariant algorithm was used.An EEG denoising method based on TI empirical mode decomposition adaptive threshold processing was presented.EEMD threshold de-noising can effectively avoid the defects of wavelet basis selection in wavelet threshold method.The introduction of translation invariant algorithm further restrains the occurrence of modal aliasing.Experimental results of the simulated and measured signals show that the proposed method is superior to a certain extent in the denoising performance with higher SNR and correlation coefficient and lower RMSE and MPE compared to existing denoising algorithms.

关 键 词：运动想象 集成经验模态分解 平移不变算法 阈值去噪 信噪比 

分 类 号：TN911.7[电子电信—通信与信息系统]