title: An EEG blind source separation algorithm based on a weak exclusion principle.
abstract: The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.
authors.lastname: Lan Ma
authors.forename: Thierry
authors.initials: T
authors.lastname: Blu
authors.forename: William S-Y
authors.initials: WS
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D009483:Neuropsychological Tests; D012815:Signal Processing, Computer-Assisted