Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG
 
Loading...
Thumbnail Image
research article

Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG

Van Zaen, Jérôme  
•
Murray, Micah M.
•
Meuli, Reto A.
Show more
2013
PLoS ONE

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.

  • Details
  • Metrics
Type
research article
DOI
10.1371/journal.pone.0060513
Web of Science ID

WOS:000318840100081

Author(s)
Van Zaen, Jérôme  
•
Murray, Micah M.
•
Meuli, Reto A.
•
Vesin, Jean-Marc  
Date Issued

2013

Publisher

Public Library of Science

Published in
PLoS ONE
Volume

8

Issue

4

Article Number

e60513

URL

URL

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0060513
Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SCI-STI-JMV  
Available on Infoscience
July 2, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/93175
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés