Automatic Removal of Artifacts from Attention Deficit Hyperactivity Disorder Electroencephalograms Based on Independent Component Analysis

Abstract

In this paper, we present an automatic artifact removal method by combining independent component analysis (ICA) and wavelet denoising to remove artifacts from attention deficit hyperactivity disorder (ADHD) electroencephalograms (EEGs). First, the ICA algorithm is used to analyze EEG signals to get the independent components (ICs); then, the wavelet method is applied to the demixed ICs as an intermediate step; finally, the EEG data are reconstructed from the new ICs (that are obtained in the intermediate step) by using the inverse ICA. The experimental results show that the automatic artifact removal method can not only remove eye artifacts, muscle artifacts and some unknown physiological artifacts sources, but also efficiently keep the weak neural activities from strong background artifacts. The results can establish a basis for ADHD clinical diagnose study.