Χαρακτηρίζοντας τις διαφορές της δυναμικής λειτουργικής συνδεσιμότητας κατά τη γήρανση ενηλίκων χρησιμοποιώντας προχωρημένες τεχνικές ανάλυσης ηλεκτροεγκεφαλογραφήματος και μηχανικής μάθησης σε κατάσταση ηρεμίας

Σαλής, Χρήστος Ηλ.

Characterizing the differences in dynamic functional connectivity of aging in adults using advanced EEG analysis techniques and machine learning in resting state

Master Thesis

Author

Σαλής, Χρήστος Ηλ.

Date

2017

Abstract

The current dissertation deals with the problem of age prediction on a set of individuals with the help of regression analysis, and grouping them into two age groups, using the functional connectivity of the electroencephalography (EEG). Specifically, the cross frequency coupling was studied in those EEG signals, while dimension reduction and vector quantization techniques were applied in the graphs that came up, in order to make their processing easier. Finally, several metrics were extracted from those graphs, which were introduced as inputs into appropriate classifiers, in order to make the age prediction and the age grouping feasible.

Postgraduate Studies Programme

Προηγμένα Συστήματα Πληροφορικής

Department

Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Πληροφορικής

Number of pages

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/10290

Collections

Τμήμα Πληροφορικής

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