A study was performed to investigate and compare the relative performance of blind signal separation (BSS) algorithms at separating common types of contamination from EEG. The study develops a novel framework for investigating and comparing the relative performance of BSS algorithms that incorporates a realistic EEG simulation with a known mixture of known signals and an objective performance metric. The key finding is that although BSS is an effective and powerful tool for separating and removing contamination from EEG, the quality of the separation is highly dependant on the type of contamination, the degree of contamination, and the choice of BSS algorithm. BSS appears to be most effective at separating muscle and blink contamination and less effective at saccadic and tracking contamination. For all types of contamination, principal components analysis is a strong performer when the contamination is greater in amplitude than the brain signal whereas other algorithms such as second-order blind inference and Infomax are generally better for specific types of contamination of lower amplitude.
*Cognitive Neuroscience Laboratory, School of Psychology, and †School of Informatics and Engineering, Flinders University, Adelaide, South Australia.
Address correspondence and reprint requests to S. P. Fitzgibbon, Cognitive Neuroscience Laboratory, School of Psychology, Flinders University, PO Box 2100, Adelaide, South Australia 5001.