Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition  

作　　者：Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie 

机构地区：[1]Department of Mechanical Engineering,Anhui University of Technology,Anhui,Ma’anshan 243002,PR China [2]College of Civil Engineering and Architecture,Anhui Polytechnic University,Anhui,Wuhu 241000,PR China [3]Department of Computer Science and Technology,Anhui University of Technology,Anhui,Ma’anshan 243002,PR China [4]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Anhui,Hefei,230000,PR China [5]Department of Management science and Engineering,Anhui University of Technology,Anhui,Ma’anshan 243002,PR China

出　　处：《China Communications》2022年第2期47-61,共15页中国通信（英文版）

基　　金：supported in part by the Science and Technology Major Project of Anhui Province(Grant No.17030901037);in part by the Humanities and Social Science Fund of Ministry of Education of China(Grant No.19YJAZH098);in part by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China(Grant Nos.GXXT-2020-012,GXXT-2021-044);in part by Science and Technology Planning Project of Wuhu City,Anhui Province,China(Grant No.2021jc1-2);part by Research Start-Up Fund for Introducing Talents from Anhui Polytechnic University(Grant No.2021YQQ066).

摘　　要：Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

关 键 词：ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning 

分 类 号：TN911.7[电子电信—通信与信息系统] R318[电子电信—信息与通信工程]