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dc.contributor.authorde Jesus Euan Campos, Carolina
dc.contributor.authorOmbao, Hernando
dc.contributor.authorOrtega, Joaquín
dc.date.accessioned2018-05-14T13:37:07Z
dc.date.available2018-05-14T13:37:07Z
dc.date.issued2018-05-03
dc.identifier.citationEuán C, Ombao H, Ortega J (2018) Spectral synchronicity in brain signals. Statistics in Medicine. Available: http://dx.doi.org/10.1002/sim.7695.
dc.identifier.issn0277-6715
dc.identifier.pmid29726025
dc.identifier.doi10.1002/sim.7695
dc.identifier.urihttp://hdl.handle.net/10754/627863
dc.description.abstractThis 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.
dc.description.sponsorshipThe authors thank the referees for their comments that led to a significant improvement of this work. This work was partially supported by (1) CONACYT, México, scholarship AS visiting research student; (2) CONACYT, México, Proyectos 169175Análisis Estadístico de Olas Marinas, Fase II, and 234057Análisis Espectral, Datos Funcionales y Aplicaciones; and (3) Centro de Investigación en Matemáticas (CIMAT). A.C. Euán wishes to thank the UC Irvine Space Time Modeling Group for the invitation to collaborate as a visiting scholar in their research group. This research was initiated at UC Irvine and completed at the King Abdullah University of Science and Technology (KAUST). The authors thank Dr Steven C. Cramer of the UC Irvine Department of Neurology for sharing the EEG data used in this paper. This work was done while J.O. was visiting, on sabbatical leave from CIMAT and with support from CONACYT, México, and the Departamento de Estadística e I.O., Universidad de Valladolid. Their hospitality and support are gratefully acknowledged.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/sim.7695
dc.subjectTime Series Clustering
dc.subjectEeg Data
dc.subjectBrain Signals
dc.subjectHierarchical Spectral Merger
dc.subjectSpectral Synchronicity
dc.subjectTotal Variation Distance
dc.titleSpectral synchronicity in brain signals
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistics in Medicine
dc.contributor.institutionCentro de Investigación en Matemáticas (CIMAT); Guanajuato Mexico
dc.identifier.arxivid1507.05018
kaust.personde Jesus Euan Campos, Carolina
kaust.personOmbao, Hernando
dc.date.published-online2018-05-03
dc.date.published-print2018-08-30


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