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  • ParkEckleyOmbao2014

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Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes

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Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes. / Park, Timothy Alexander; Eckley, Idris; Ombao, Hernando.

In: IEEE Transactions on Signal Processing, Vol. 62, No. 20, 15.10.2014, p. 5240 - 5250.

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Park, Timothy Alexander ; Eckley, Idris ; Ombao, Hernando. / Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes. In: IEEE Transactions on Signal Processing. 2014 ; Vol. 62, No. 20. pp. 5240 - 5250.

Bibtex

@article{f523ce714a8d43bbb581c85b0bc16727,
title = "Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes",
abstract = "We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorously define and estimate two forms of cross-dependence measures: wavelet coherence and wavelet partial coherence. These dependence measures differ in a subtle but important way. The former is a broad measure of dependence, which may include indirect associations, i.e., dependence between a pair of signals that is driven by another signal. Conversely, wavelet partial coherence measures direct linear association between a pair of signals, i.e., it removes the linear effect of other observed signals. Our time-scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals, as we demonstrate on electroencephalograms recorded in a visual–motor experiment.",
author = "Park, {Timothy Alexander} and Idris Eckley and Hernando Ombao",
note = "c IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2014",
month = "10",
day = "15",
doi = "10.1109/TSP.2014.2343937",
language = "English",
volume = "62",
pages = "5240 -- 5250",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "20",

}

RIS

TY - JOUR

T1 - Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes

AU - Park, Timothy Alexander

AU - Eckley, Idris

AU - Ombao, Hernando

N1 - c IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2014/10/15

Y1 - 2014/10/15

N2 - We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorously define and estimate two forms of cross-dependence measures: wavelet coherence and wavelet partial coherence. These dependence measures differ in a subtle but important way. The former is a broad measure of dependence, which may include indirect associations, i.e., dependence between a pair of signals that is driven by another signal. Conversely, wavelet partial coherence measures direct linear association between a pair of signals, i.e., it removes the linear effect of other observed signals. Our time-scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals, as we demonstrate on electroencephalograms recorded in a visual–motor experiment.

AB - We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorously define and estimate two forms of cross-dependence measures: wavelet coherence and wavelet partial coherence. These dependence measures differ in a subtle but important way. The former is a broad measure of dependence, which may include indirect associations, i.e., dependence between a pair of signals that is driven by another signal. Conversely, wavelet partial coherence measures direct linear association between a pair of signals, i.e., it removes the linear effect of other observed signals. Our time-scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals, as we demonstrate on electroencephalograms recorded in a visual–motor experiment.

U2 - 10.1109/TSP.2014.2343937

DO - 10.1109/TSP.2014.2343937

M3 - Journal article

VL - 62

SP - 5240

EP - 5250

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 20

ER -