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Spectral synchronicity in brain signals

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Spectral synchronicity in brain signals. / Carolina, Euán; Hernando, Ombao; Joaquin, Ortega.
In: Statistics in Medicine, 30.08.2018, p. 1-19.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Carolina, E, Hernando, O & Joaquin, O 2018, 'Spectral synchronicity in brain signals', Statistics in Medicine, pp. 1-19. https://doi.org/10.1002/sim.7695

APA

Carolina, E., Hernando, O., & Joaquin, O. (2018). Spectral synchronicity in brain signals. Statistics in Medicine, 1-19. https://doi.org/10.1002/sim.7695

Vancouver

Carolina E, Hernando O, Joaquin O. Spectral synchronicity in brain signals. Statistics in Medicine. 2018 Aug 30;1-19. doi: 10.1002/sim.7695

Author

Carolina, Euán ; Hernando, Ombao ; Joaquin, Ortega. / Spectral synchronicity in brain signals. In: Statistics in Medicine. 2018 ; pp. 1-19.

Bibtex

@article{c38660bb140c43c486e09f171d6dd9fa,
title = "Spectral synchronicity in brain signals",
abstract = "This paper addresses the problem of identifying brain regions with similar oscillatory patterns detected from electroencephalograms. We introduce the hierarchical spectral merger (HSM) clustering method where the feature of interest is the spectral curve and the similarity metric used is the total variance distance. The HSM method is compared with clustering using features derived from independent‐component analysis. Moreover, the HSM method is applied to 2 different electroencephalogram datasets. The first was recorded at resting state where the participant was not engaged in any cognitive task; the second was recorded during a spontaneous epileptic seizure. The results of the analyses using the HSM method demonstrate that clustering could evolve over the duration of the resting state and during epileptic seizure.",
keywords = "brain signals, EEG data, hierarchical spectral merger, spectral synchronicity, time series clustering, total variation distance",
author = "Eu{\'a}n Carolina and Ombao Hernando and Ortega Joaquin",
year = "2018",
month = aug,
day = "30",
doi = "10.1002/sim.7695",
language = "English",
pages = "1--19",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",

}

RIS

TY - JOUR

T1 - Spectral synchronicity in brain signals

AU - Carolina, Euán

AU - Hernando, Ombao

AU - Joaquin, Ortega

PY - 2018/8/30

Y1 - 2018/8/30

N2 - This paper addresses the problem of identifying brain regions with similar oscillatory patterns detected from electroencephalograms. We introduce the hierarchical spectral merger (HSM) clustering method where the feature of interest is the spectral curve and the similarity metric used is the total variance distance. The HSM method is compared with clustering using features derived from independent‐component analysis. Moreover, the HSM method is applied to 2 different electroencephalogram datasets. The first was recorded at resting state where the participant was not engaged in any cognitive task; the second was recorded during a spontaneous epileptic seizure. The results of the analyses using the HSM method demonstrate that clustering could evolve over the duration of the resting state and during epileptic seizure.

AB - This paper addresses the problem of identifying brain regions with similar oscillatory patterns detected from electroencephalograms. We introduce the hierarchical spectral merger (HSM) clustering method where the feature of interest is the spectral curve and the similarity metric used is the total variance distance. The HSM method is compared with clustering using features derived from independent‐component analysis. Moreover, the HSM method is applied to 2 different electroencephalogram datasets. The first was recorded at resting state where the participant was not engaged in any cognitive task; the second was recorded during a spontaneous epileptic seizure. The results of the analyses using the HSM method demonstrate that clustering could evolve over the duration of the resting state and during epileptic seizure.

KW - brain signals

KW - EEG data

KW - hierarchical spectral merger

KW - spectral synchronicity

KW - time series clustering

KW - total variation distance

U2 - 10.1002/sim.7695

DO - 10.1002/sim.7695

M3 - Journal article

SP - 1

EP - 19

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

ER -