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

Whitcher, B., Craigmile, P.F. and Brown, P. (2005) Time-varying spectral analysis in neurophysiological time series using Hilbert wavelet pairs. Signal Processing, 85(11), pp. 2065-2081. (doi: 10.1016/j.sigpro.2005.07.002)

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.

• Item Type: Articles
• Additional Information: Special Issue: Neuronal Coordination in the Brain: A Signal Processing Perspective
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Craigmile, Dr Peter
• Authors: Whitcher, B., Craigmile, P.F., and Brown, P.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Signal Processing
• ISSN: 0165-1684