An Automatic Removal Method of Ocular Artifacts in EEG

Abstract

Electrooculogram (EOG) is an inevitable main interference in electroencephalogram (EEG) acquisition, which directly affects the analysis and application of EEG. Second-order blind identification (SOBI), as a blind source separation (BSS), has been used to remove the ocular artifacts (OA) of contaminated EEG. However, SOBI that assumes the source signal to be stationary is not appropriate for nonstationary EEG signals, yielding undesirable separation results. In addition, it is regrettable that the current discriminations of ocular artifacts, such as correlation coefficients, sample entropy, do not take into account of the fuzzy characteristics of EEG, which leads to the inaccurate judgement of OA. In this paper, a novel OA removal method is proposed based on the combination of discrete wavelet transform (DWT) and SOBI and denoted as DWSOBI. DWT is used to analyze each channel of contaminated EEG to obtain more stable multi-scale wavelet coefficients; then, the wavelet coefficients in the same layer are selected to construct the wavelet coefficient matrix, and it is further separated by using SOBI to obtain the estimation of source signals, whose fuzzy entropies are calculated and employed to realize the automatic identification and removal of OA. Based on a public database, many experiments are conducted and two performance indexes are adopted to measure the elimination effect of OA. The experiment results show that DWSOBI achieves more adaptive and accurate performance for four kinds of OA from three subjects, and is superior to the commonly used methods.