Evolutionary coherence of nonstationary signals

Hernando Ombao*, Sébastien Van Bellegem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Coherence is a widely used measure for characterizing linear dependence between a pair of signals. For nonstationary signals, the autospectrum, cross spectrum, and coherence between signals may evolve over time. A standard approach is to divide the signals into overlapping blocks of fixed width and then smooth (over frequency) the periodogram matrix at each time block. In this paper, a consistent estimation procedure is developed using time-localized linear filtering. The proposed method automatically selects, via repeated tests of homogeneity, the optimal window width for estimating local coherence. It is pointwise adaptive in the sense that the width of the optimal interval is allowed to change across time. Under the locally stationary process framework, we develop a central limit theorem on the Fisher-z transform of our time-localized band coherence. We apply our method to a pair of highly dynamic brain waves signals whose coherence is shown to evolve during an epileptic seizure.

Original languageEnglish (US)
Pages (from-to)2259-2266
Number of pages8
JournalIEEE Transactions on Signal Processing
Issue number6
StatePublished - Jun 1 2008


  • Coherence
  • Data-adaptive windowing
  • Linear filtering
  • Nonstationary signals

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Evolutionary coherence of nonstationary signals'. Together they form a unique fingerprint.

Cite this