Removal of ocular artifacts from electroencephalo-graph by improving variational mode decomposition

Abstract

Ocular artifacts in Electroencephalography (EEG) recordings lead to inaccurate results in signal analysis and process. Variational Mode Decomposition (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 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.