Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Détails

Ressource 1Télécharger: BIB_275575A06E81.P001.pdf (1497.43 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_275575A06E81
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Adaptive filtering methods for identifying cross-frequency couplings in human EEG.
Périodique
Plos One
Auteur(s)
Van Zaen J., Murray M.M., Meuli R.A., Vesin J.M.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Statut éditorial
Publié
Date de publication
2013
Volume
8
Numéro
4
Pages
e60513
Langue
anglais
Notes
Publication types: Journal Article Publication Status: ppublish
Résumé
Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.
Pubmed
Web of science
Open Access
Oui
Création de la notice
09/04/2013 13:16
Dernière modification de la notice
20/08/2019 13:06
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