Τεχνικές επεξεργασίας και ανάλυσης ηλεκτροεγκεφαλικών σημάτων για την αξιολόγηση της γνωστικής απόκρισης του αθρώπινου εγκεφάλου

Γιαννόπουλος, Αναστάσιος; Giannopoulos, Anastasios

dc.contributor.author	Γιαννόπουλος, Αναστάσιος	el
dc.contributor.author	Giannopoulos, Anastasios	en
dc.date.accessioned	2024-02-06T10:08:10Z
dc.date.available	2024-02-06T10:08:10Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58791
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.26487
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Electroencephalography	en
dc.subject	Cognitive Electrophysiology	en
dc.subject	Event-related Potential	en
dc.subject	Neuroimaging	en
dc.subject	Statistical Analysis	en
dc.subject	Graph Theory	en
dc.title	Τεχνικές επεξεργασίας και ανάλυσης ηλεκτροεγκεφαλικών σημάτων για την αξιολόγηση της γνωστικής απόκρισης του αθρώπινου εγκεφάλου	el
dc.title	Electroencephalographic signal processing and analysis techniques for evaluating the cognitive response of human brain	en
heal.type	doctoralThesis
heal.generalDescription	Standarization and Deployment of a generalized brain signal processing techniques for assessing the brain electrical activity, biomarker identification, development of advanced techniques for statistics, brain differences between clinical and control groups, study on the brain signature of Body Dysmorphic Disorder, research on Prepulse Inhibition of the Sensorimotor System, research on the Decision-making process about Optical Illusions from the brain perspective	en
heal.classification	Brain Signal Processing	en
heal.classification	Statistics	en
heal.classification	Brain Electrical Activity	en
heal.classification	Brain Biomarker	el
heal.classification	Electroencephalography	el
heal.language	el
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-11-21
heal.abstract	This PhD thesis was composed based on a series of technical and experimental modules, mainly aimed at presenting and applying advanced brain signal processing methods in a variety of clinical experiments. Throughout this thesis, the central goal was to develop an integrated and automated sequence of steps towards the (partial) decipherment of the most complex system found in nature: the human brain. The dominant purpose of the techniques presented was to demonstrate the synergy between Engineering, Statistics and Neuroscience in order to draw conclusions about the cognitive response of the human brain. It is worth noting that the procedure applied in each of the chapters dealing with specific experiments is of general purpose, therefore it can be reused in a variety of clinical or non-brain studies. Clearly, modifications to the methods of analysis are likely to be required, depending on the particular technical or theoretical changes resulting from the particular experiment. In the first chapter, the basic techniques of recording brain signals were presented, highlighting the main features and advantages of the Electroencephalogram (EEG) technique. In the second chapter, a standard sequence of steps towards noise mitigation in EEG signals was given, as well as standard processing techniques. The third chapter presents all those statistical techniques needed to validate results and draw conclusions in EEG studies. In the fourth chapter, the evoked potentials during the brain activity of past or future self-viewing were studied in order to elucidate whether or not past and future thinking share common brain resources. In chapter five, using the PPI/PPF experiment, three different studies on the sensorimotor system were presented. The latter is involved in any process involving the manifestation of a form of startle-motion and sensory stimuli. Next, we examined the neural correlates of BDD by investigating the electrophysiological responses to PPI and PPF auditory stimuli. Using a similar experiment, we studied startle responses in children and adolescents. Finally, in chapter six, we investigated the sensitivity of BDD patients to visual illusory phenomena and their corresponding brain connectivity patterns while making judgments about the corresponding visual stimuli.	en
heal.advisorName	Καψάλης, Χρήστος	el
heal.advisorName	Capsalis, Christos	en
heal.committeeMemberName	Capsalis, Christos	en
heal.committeeMemberName	Cottis, Panagiotis	en
heal.committeeMemberName	Trakadas, Panagiotis	en
heal.committeeMemberName	Fikioris, Giorgos	en
heal.committeeMemberName	Papageorgiou, Charalabos	en
heal.committeeMemberName	Panagopoulos, Athanasios	en
heal.committeeMemberName	Kaklamani, Dimitra	en
heal.committeeMemberName	Καψάλης, Χρήστος	el
heal.committeeMemberName	Κωττής, Παναγιώτης	el
heal.committeeMemberName	Τρακάδας, Παναγιώτης	el
heal.committeeMemberName	Φικιώρης, Γεώργιος	el
heal.committeeMemberName	Παπαγεωργίου, Χαράλαμπος	el
heal.committeeMemberName	Παναγόπουλος, Αθανάσιος	el
heal.committeeMemberName	Κακλαμάνη, Δήμητρα	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Συστημάτων Μετάδοσης Πληροφορίας και Τεχνολογίας Υλικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	212 σ.	el
heal.fullTextAvailability	false