Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/3D7865AC-516D-436D-8084-16463509AD5B | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.69327 | - |
| Language | en | - |
| Extent | 105 pages | el |
| Extent | 990 kilobytes | en |
| Extent | A4 (210x297mm) | en |
| Title | Tensor-based fMRI signal processing | en |
| Title | Επεξεργασία fMRI με χρήση Tensors | el |
| Creator | Karakasis Paris | en |
| Creator | Καρακασης Παρις | el |
| Contributor [Thesis Supervisor] | Liavas Athanasios | en |
| Contributor [Thesis Supervisor] | Λιαβας Αθανασιος | el |
| Contributor [Committee Member] | Zervakis Michalis | en |
| Contributor [Committee Member] | Ζερβακης Μιχαλης | el |
| Contributor [Committee Member] | Karystinos Georgios | en |
| Contributor [Committee Member] | Καρυστινος Γεωργιος | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Description | Διπλωματική εργασία που υποβλήθηκε στη σχολή ΗΜΜΥ του Πολ. Κρήτης για την πλήρωση προϋποθέσεων λήψης του Διπλώματος Ηλεκτρολόγου Μηχανικού και Μηχανικού Υπολογιστών. | el |
| Content Summary | Functional 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 Item | Διπλωματική Εργασία | el |
| Type of Item | Diploma Work | en |
| License | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| Date of Item | 2017-09-21 | - |
| Date of Publication | 2017 | - |
| Subject | Nonnegative Tensor Factorization | en |
| Subject | NTF | en |
| Subject | Parafac | en |
| Subject | BSS | en |
| Subject | Blind Source Separation | en |
| Subject | Tensor decompotion models | en |
| Subject | fMRI | en |
| Subject | Functional Magnetic Resonance Imaging | en |
| Bibliographic Citation | Πάρις Καρακάσης, "Επεξεργασία fMRI με χρήση Tensors", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017 | el |
| Bibliographic Citation | Paris Karakasis, "Tensor-based fMRI processing signal processing", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017 | en |
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