Whitening of Background Brain Activity via Parametric Modeling

Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities of two parametric techniques: a direct one based on Levinson solution of Yule-Walker equations, called AR Yule-Walker, and an indirect one based on the least-squares solution of forward-backward linear prediction (FBLP) equations, called AR-FBLP. The whitening effect of the two algorithms is investigated with real background electroencephalogram (EEG) colored noise and compared in time and frequency domains.