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Sci Rep. 2019 May 14;9(1):7389. doi: 10.1038/s41598-019-43571-2.

Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data.

Author information

1
CNRS UMR-7225, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France. neurodynamicslab@gmail.com.
2
IRD-UPMC UMI-209, UMMISCO, 93143, Bondy, France.
3
CNRS UMR-8197, IBENS, Ecole Normale Supérieure, 75005, Paris, France.

Abstract

Time series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet analysis and non-stationary surrogates to detect short-lived spatial coherent patterns from multivariate time-series. In contrast with standard methods, the surrogate data proposed here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. We evaluate this framework on synthetic and real-world time series, and we show that it can provide useful insights into the time-resolved structure of spatially extended systems.

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