Subtraction of Ocular and Line Artifacts from Mental EEG based on ICA

Abstract

The electroencephalogram (EEG) is a set of data measured by electrodes placed on the scalp and is often under the influences of artifacts. Mental EEG is recorded when a person performs different mental tasks. In this article, we separated the mental EEG signals into independent components with individual meanings based on Independent Component Analysis (ICA) method, and the EEG was reconstructed by excluding those components related to ocular and line artifacts for further feature extraction. In the end, we made a simulation to compare denoising of mental EEG using ICA with the result using wavelet packet analysis. The ICA method was found to be more efficient than the classical methods.