EEG artifact removal and noise suppression by Discrete Orthonormal S-Transform denoising

Abstract

Electroencephalographic data is often contaminated by artifacts produced from non-cerebral sources such as muscle activity, eye movements and other electrical disturbances. Artifact removal and noise suppression is essential prior to information extraction from Electroencephalogram signals. In this paper, a novel automated methodology of artifact removal and noise suppression of Electroencephalogram data is proposed. The proposed methodology relies on two distinct techniques such as Independent Component Analysis and Discrete Orthonormal S-Transform. The method is carried out in four methodological steps viz. Blind Source Separation of artifact contaminated Electroencephalographic data in independent components, automated identification of artifactual independent components, Discrete Orthonormal S-Transform denoising of artifactual independent components and reconstruction of clean Electroencephalogram data. The efficacy of the methodology is evaluated on simulated as well as experimental artifactual Electroencephalogram data. The proposed methodology effectively removed large proportion of artifacts and noise while preserved useful cerebral activity.