Functional Parcellation of Memory Related Brain Networks by Spectral Clustering of EEG Data

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dc.authorid0000-0002-7216-1079
dc.authorscopusid57216204910
dc.authorscopusid48061412400
dc.authorscopusid55370597700
dc.authorscopusid6603952578
dc.authorscopusid6603839786
dc.authorwosidÇiftçi, Koray/ABA-6527-2020
dc.contributor.authorAydın, Çağatay
dc.contributor.authorOktay, Oytun
dc.contributor.authorGünebakan, Adem Umut
dc.contributor.authorÇiftçi, Rifat Koray
dc.contributor.authorAdemoglu, Ahmet
dc.date.accessioned2022-05-11T14:10:28Z
dc.date.available2022-05-11T14:10:28Z
dc.date.issued2012
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.description35th International Conference on Telecommunications and Signal Processing (TSP) -- JUL 03-04, 2012 -- Prague, CZECH REPUBLIC
dc.description.abstractIn this study, we investigate the clustering information of alpha band brain networks during memory load task. For this purpose, short time memory task which includes memory load varieties is implemented to the subjects. To calculate mutual information, time and frequency information is both taken into consideration due to Cohen class time-frequency distribution (TFD) formulation. Cohen class based mutual information helps us to integrate adjacency matrices based on the similarity information of individual electrode pairs. In addition, essential frequency bins are selected from the TFD with respect to the default alpha frequency (8 - 12Hz) intervals. Moreover, graph based spectral clustering algorithm is used to parcellate memory related circuits on the brain. From the calculated adjacency matrices, the N-cut algorithm is used for node wise clustering between nodes. After node wise clustering information, subject wise clustering is applied with respect to the similarities of node information over all subjects.
dc.description.sponsorshipBrno Univ Technol, Dept Telecommun, Budapest Univ Technol & Econom, Dept Telecommun & Media Informat, Karadeniz Tech Univ, Dept Elect & Elect Engn, W Pomeranian Univ Technol, Fac Elect Engn, VSB - Tech Univ Ostrava, Dept Telecommun, Slovak Univ Technol, Inst Telecommun, Univ Ljubljana, Lab Telecommun, Czech Tech Univ Prague, Dept Telecommun Engn, ProfiNET Test s r o, NextiraOne Czech s r o, IEEE, Czechoslovakia Sect, Investment & Business Dev Agcy Czech Republ (CzechInvest), IEEE
dc.description.sponsorshipScientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [109E202]
dc.description.sponsorshipThis study is supported by Scientific and Technological Research Council of Turkey (TUB ?ITAK) under project number 109E202.
dc.identifier.endpage585
dc.identifier.isbn978-1-4673-1118-2
dc.identifier.scopus2-s2.0-84866949178
dc.identifier.startpage581
dc.identifier.urihttps://hdl.handle.net/20.500.11776/5412
dc.identifier.wosWOS:000308143100113
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorOktay, Oytun
dc.institutionauthorÇiftçi, Rifat Koray
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2012 35th International Conference on Telecommunications and Signal Processing (Tsp)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEEG
dc.subjectMemory Load
dc.subjectMutual Information
dc.subjectNormalized Cut
dc.subjectWorking Memory
dc.subjectMutual Information
dc.subjectAlpha
dc.subjectOscillations
dc.subjectIncrease
dc.titleFunctional Parcellation of Memory Related Brain Networks by Spectral Clustering of EEG Data
dc.typeConference Object

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