A hybrid method for artifact removal of visual evoked EEG

Abstract

BackgroundThe visual evoked Electroencephalogram (EEG) signals are useful indicators to explore the hidden neural circuitry in human brain. But these signals are highly contaminated with a plethora of artifacts arising from power interference, eye, muscle and cardiac movements. Since the interference components include neural activity also, the existing techniques result in the distortion of the underlying cerebral signals. New MethodTo address the aforementioned problem, the current study proposes a hybrid method for denoising the visually evoked EEG responses. According to the proposed method, a cascade combination of digital filters, Independent Component Analysis (ICA) and Transient Artifact Reduction Algorithm (TARA) is utilized to suppress the artifacts. ICA technique automatically eliminates the ocular artifacts. The interference due to the remaining artifacts is removed through TARA. ResultsThe artifact removal ability of the proposed heuristics is evaluated in terms of SNR, correlation coefficient and sample entropy. The ICA results exhibit an increase of 13.47 % in SNR values on simulated signals and 26.66 % on real data. The application of TARA on simulated and real signals results in further SNR gain of 6.98 % and 71.51 % respectively. Significant statistical difference is also observed in this method (p<0.05). Comparison with Existing MethodsThis approach outperforms previous methods based on wavelets, enhanced variants of empirical mode decomposition and earlier versions of total variation denoising. ConclusionICA-TARA effectively eliminates the major artifacts without compromising the interpretation of the underlying neural state in both simulated and real visual evoked EEG.