Removing electroencephalographic artifacts: Comparison between ICA and PCA

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 an Independent Component Analysis (ICA) algorithm [2, 12] 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 artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis.