EEG artifact reduction in VEP using 2-stage PCA and N4 analysis of alcoholics

In this paper, repeated applications of Principal Component Analysis (PCA) are proposed to reduce background electroencephalogram (EEG) artifact from multi-channel and multi-trial Visual Evoked Potential (VEP) signals. This will allow single trial analysis of VEP signals. PCA has been used for noise reduction but the method of repeated applications of PCA is novel. In the study here, PCA was applied in 2 stages. In the first stage, PCA was applied to multi-channel VEP signals from one trial. The output VEP signals from the first stage were used in the second stage, where PCA was applied to multi-trial VEP signals from a single channel. Simulation study using emulated VEP signals contaminated with EEG artifact shows significant improvement in signal to noise ratio using the method. It was then applied to study the electrophysiological differences between alcoholic and nonalcoholic subjects using N4 parameter. Hypothesis testing using t-test showed that alcoholics had significantly weaker and slower N4 responses as compared to non-alcoholics.