Time-varying spectral analysis in neurophysiological time series using Hilbert wavelet pairs

  • Authors:

  • Brandon Whitcher;Peter F. Craigmile;Peter Brown

  • Affiliations:

  • Translational Medicine & Genetics, GlaxoSmithKline, Greenford, United Kingdom;Department of Statistics, The Ohio State University, Columbus, OH;Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, United Kingdom

  • Venue:
  • Signal Processing - Neuronal coordination in the brain: A signal processing perspective
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

An analytic wavelet transform, based on Hilbert wavelet pairs, is applied to bivariate time-varying spectral estimation for neurophysiological time series. Under the assumption of an underlying block stationary process, both single-trial and ensemble studies are amenable to this method. A bootstrap procedure, which samples with replacement blocks centered around the events of interest, is proposed to identify time points for which the event-averaged magnitude squared coherence is non-zero. Clinical data sets are used to compare the wavelet-based technique with the classical Fourier-based spectral measures and highlight its ability to detect time-varying coherence and phase properties.