title: Adaptive filtering methods for identifying cross-frequency couplings in human EEG.
abstract: 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.
authors.affiliation: Applied Signal Processing Group, Swiss Federal Institute of Technology, Lausanne, Switzerland. jerome.vanzaen@epfl.ch
authors.forename: Jérôme
authors.initials: J
authors.lastname: Van Zaen
authors.forename: Micah M
authors.initials: MM
authors.lastname: Murray
authors.forename: Reto A
authors.initials: RA
authors.lastname: Meuli
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Vesin
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
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