Removing electroencephalographic artifacts: comparison between ICA and PCA

Abstract

Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of the independent component analysis (ICA) algorithm for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifact sources in EEG records with results comparing favourably to those obtained using principal component analysis (PCA).