Whitening of Background Brain Activity via Parametric Modeling | Zendy

Nidal Kamel | Zendy; Andrews Samraj | Zendy; Arash Mousavi | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Whitening of Background Brain Activity via Parametric Modeling

Author(s) -

Nidal Kamel,

Andrews Samraj,

Arash Mousavi

Publication year - 2007

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2007/48720

Subject(s) - colors of noise , colored , noise (video) , computer science , parametric statistics , white noise , parametric model , signal (programming language) , subspace topology , electroencephalography , pattern recognition (psychology) , algorithm , speech recognition , mathematics , artificial intelligence , statistics , image (mathematics) , psychology , telecommunications , materials science , psychiatry , composite material , programming language

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research