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dc.contributor.authorChen, Tianbo
dc.contributor.authorSun, Ying
dc.contributor.authorde Jesus Euan Campos, Carolina
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2020-11-26T05:23:42Z
dc.date.available2020-08-17T13:57:55Z
dc.date.available2020-11-26T05:23:42Z
dc.date.issued2020-11-18
dc.identifier.citationChen, T., Sun, Y., Euan, C., & Ombao, H. (2020). Clustering Brain Signals: a Robust Approach Using Functional Data Ranking. Journal of Classification. doi:10.1007/s00357-020-09382-1
dc.identifier.issn1432-1343
dc.identifier.issn0176-4268
dc.identifier.doi10.1007/s00357-020-09382-1
dc.identifier.urihttp://hdl.handle.net/10754/664643
dc.description.abstractIn this paper, we analyze electroencephalograms (EEGs) which are recordings of brain electrical activity. We develop new clustering methods for identifying synchronized brain regions, where the EEGs show similar oscillations or waveforms according to their spectral densities. We treat the estimated spectral densities from many epochs or trials as functional data and develop clustering algorithms based on functional data ranking. The two proposed clustering algorithms use different dissimilarity measures: distance of the functional medians and the area of the central region. The performance of the proposed algorithms is examined by simulation studies. We show that, when contaminations are present, the proposed methods for clustering spectral densities are more robust than the mean-based methods. The developed methods are applied to two stages of resting state EEG data from a male college student, corresponding to early exploration of functional connectivity in the human brain.
dc.description.sponsorshipThe authors thank Professor Wu for sharing the EEG data set. The authors would also like to thank the Editor and the anonymous Associate Editor for their suggestions.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/s00357-020-09382-1
dc.rightsArchived with thanks to Journal of Classification
dc.titleClustering Brain Signals: a Robust Approach Using Functional Data Ranking
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Classification
dc.rights.embargodate2021-11-26
dc.eprint.versionPost-print
dc.identifier.arxivid2007.14078
kaust.personChen, Tianbo
kaust.personSun, Ying
kaust.personde Jesus Euan Campos, Carolina
kaust.personOmbao, Hernando
dc.date.accepted2020-10-30
dc.identifier.eid2-s2.0-85096318865
refterms.dateFOA2020-08-17T13:58:26Z
dc.date.posted2020-07-28


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