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Tensor-based fMRI signal processing

Karakasis Paris

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URIhttp://purl.tuc.gr/dl/dias/3D7865AC-516D-436D-8084-16463509AD5B-
Identifierhttps://doi.org/10.26233/heallink.tuc.69327-
Languageen-
Extent105 pagesel
Extent990 kilobytesen
ExtentA4 (210x297mm)en
TitleTensor-based fMRI signal processingen
TitleΕπεξεργασία fMRI με χρήση Tensorsel
CreatorKarakasis Parisen
CreatorΚαρακασης Παριςel
Contributor [Thesis Supervisor]Liavas Athanasiosen
Contributor [Thesis Supervisor]Λιαβας Αθανασιοςel
Contributor [Committee Member]Zervakis Michalisen
Contributor [Committee Member]Ζερβακης Μιχαληςel
Contributor [Committee Member]Karystinos Georgiosen
Contributor [Committee Member]Καρυστινος Γεωργιοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
DescriptionΔιπλωματική εργασία που υποβλήθηκε στη σχολή ΗΜΜΥ του Πολ. Κρήτης για την πλήρωση προϋποθέσεων λήψης του Διπλώματος Ηλεκτρολόγου Μηχανικού και Μηχανικού Υπολογιστών.el
Content SummaryFunctional magnetic resonance imaging (fMRI) is one of the most popular methods in studying the human brain. fMRI provides a non-invasive way to measure brain activity, detecting local changes of blood oxygen level density (BOLD) in the brain, over time. The purpose of fMRI signal analysis is the localization of brain areas that are related with particular tasks. The problem of fMRI signal analysis can be considered as a blind source separation problem (BSS) , which is the problem of extracting a set of source signals from a set of mixed signals, without using prior information (or with very little prior information) about the source signals or the mixing process. In this thesis, initially, we study the usage of nonnegative matrix factorization models in BSS problems, assuming the existence of delays in propagation environments with or without echo. Next, we study the usage of tensor factorization models, through PARAFAC and Nonnegative Tensor Factorization models, in BSS problems, as well as how these models can be extended under the assumption of propagation environments without echo. Finally, we use these tensor factorization models in fMRI data analysis. In our analysis, we processed fMRI data from different subjects performing the same tasks (group task related fMRI analysis) and we extracted common activation brain maps as well as common activation time signals.en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by-nc/4.0/en
Date of Item2017-09-21-
Date of Publication2017-
SubjectNonnegative Tensor Factorizationen
SubjectNTFen
SubjectParafacen
SubjectBSSen
SubjectBlind Source Separationen
SubjectTensor decompotion modelsen
SubjectfMRIen
SubjectFunctional Magnetic Resonance Imagingen
Bibliographic CitationΠάρις Καρακάσης, "Επεξεργασία fMRI με χρήση Tensors", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017el
Bibliographic CitationParis Karakasis, "Tensor-based fMRI processing signal processing", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017en

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