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dc.contributor.authorde Jesus Euan Campos, Carolina
dc.contributor.authorSun, Ying
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2019-08-01T13:23:57Z
dc.date.available2019-08-01T13:23:57Z
dc.date.issued2019-06-17
dc.identifier.citationEuán, C., Sun, Y., & Ombao, H. (2019). Coherence-based time series clustering for statistical inference and visualization of brain connectivity. The Annals of Applied Statistics, 13(2), 990–1015. doi:10.1214/18-aoas1225
dc.identifier.doi10.1214/18-AOAS1225
dc.identifier.urihttp://hdl.handle.net/10754/656304
dc.description.abstractWe develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display a high level of coordination as measured by “cluster-coherence.” While the most common approach to measure dependence between clusters is through pairs of single time series, our method proposes cluster coherence which measures dependence between pairs of whole clusters rather than between single elements. Thus it takes into account both the dependence between clusters and within channels in a cluster. The identified clusters contain time series that exhibit high cross-dependence in the spectral domain. Simulation studies demonstrate that the proposed HCC method is competitive with the other feature-based clustering methods. To study clustering in a network of multichannel electroencephalograms (EEG) during an epileptic seizure, we applied the HCC method and identified connectivity on alpha (8, 12) Hertz and beta (16, 30) Hertz bands at different phases of the recording: before an epileptic seizure, during the early and middle phases of the seizure episode. To increase the potential impact of HCC in neuroscience, we also developed the HCC-Vis, an R-Shiny app (RStudio), which can be downloaded from https://carolinaeuan.shinyapps.io/hcc-vis/.
dc.publisherInstitute of Mathematical Statistics
dc.relation.urlhttps://projecteuclid.org/euclid.aoas/1560758435
dc.rightsArchived with thanks to Annals of Applied Statistics
dc.subjectCluster coherence
dc.subjectmultivariate time series
dc.subjectelectroencephalograms
dc.subjectspectral analysis
dc.subjectclassification
dc.titleCoherence-based time series clustering for statistical inference and visualization of brain connectivity
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalAnnals of Applied Statistics
dc.eprint.versionPublisher's Version/PDF
kaust.personde Jesus Euan Campos, Carolina
kaust.personSun, Ying
kaust.personOmbao, Hernando
refterms.dateFOA2019-08-01T13:25:40Z
dc.date.published-online2019-06-17
dc.date.published-print2019-06


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