Two level PCA to reduce noise and EEG from evoked potential signals

Two common artifacts that corrupt evoked responses are noise and background electroencephalogram (EEG). In this paper, a two-level principal component analysis (PCA) is used to reduce these artifacts from single trial evoked responses. The first level PCA is applied to reduce noise from these VEP signals while the second level PCA reduces EEG. The method is used to analyse the object recognition and decision-making capability during visual responses. The analysis is extended to study the differences in visual response between alcoholics and non-alcoholics using single trial P3 visual evoked potential (VEP) signals. The analysis shows that alcoholics respond slower and weaker to visual stimulus as compared to non-alcoholics.