A Fully Automatic Method for Ocular Artifact Suppression from EEG Data Using Wavelet Transform and Independent Component Analysis

Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is a general accepted tool for isolating artifactual components. One major challenge to artifact removal using ICA is the automatic identification of the artifactual components. However there is still little consensus on criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on an efficient combination of statistical and wavelet-based measures for ocular artifact suppression. The results on 420 4-s EEG epochs indicate that the artifact components can be identified correctly with 96.4%