Ocular artifacts in Electroencephalo-graphy (EEG) recordings lead to inaccurate results in signal analysis and process.Variational Mode Decom-position (VMD) is an adaptive and completely non-recursive 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 im-proved with abilities of global optimal guidance and opposition based learning.The original seasonal mon-itoring 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 ap-plied 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 com-pared with other methods.The proposed method im-proves the SNR of the dataset from-2.03 to 2.30.

Miao Shi;Chao Wang;Wei Zhao;Xinshi Zhang;Ye Ye;Nenggang Xie

Department of Mechanical Engineering,Anhui University of Technology,Anhui,Ma'anshan 243002,PR China;College of Civil Engineering and Architecture,Anhui Polytechnic University,Anhui,Wuhu 241000,PR China;Department of Computer Science and Technology,Anhui University of Technology,Anhui,Ma'anshan 243002,PR China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Anhui,Hefei,230000,PR China;Department of Management science and Engineering,Anhui University of Technology,Anhui,Ma'anshan 243002,PR China

中国通信（英文版）

[1]Miao Shi;Chao Wang;Wei Zhao;Xinshi Zhang;Ye Ye;Nenggang Xie-.Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition)[J].中国通信（英文版）,2022(02):47-61

Electroencephalo,Opposition,GOSSA

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