Evaluating multisensory integration in individuals with    autism and schizophrenia utilizing EEG data

Theodoros, Balougias; Μπαλούγιας, Θεόδωρος

dc.contributor.author	Theodoros, Balougias	en
dc.contributor.author	Μπαλούγιας, Θεόδωρος	el
dc.date.accessioned	2025-04-30T08:36:54Z
dc.date.available	2025-04-30T08:36:54Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61820
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29516
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Μεταφραστική Βιοιατρική Μηχανικής και Επιστήμης"	el
dc.rights	Default License
dc.subject	EEGNet	en
dc.subject	Electroencephalography	en
dc.subject	Schizophrenia	en
dc.subject	Autism Spectrum Disorder	en
dc.subject	Slice Tensor Component Analysis	en
dc.title	Evaluating multisensory integration in individuals with autism and schizophrenia utilizing EEG data	en
heal.type	masterThesis
heal.classification	Computational Neuroscience	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-30
heal.abstract	Multisensory integration (MSI) is the brain's ability to combine inputs from multiple sensory modalities, such as auditory and visual stimuli, to form a cohesive perceptual experience. Rapid and accurate interactions with the environment depend on effective MSI, but people with neurodevelopmental disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD) frequently struggle with this process. This thesis performs a electroencephalography (EEG) data analysis to examine the neural mechanisms of MSI in these populations relative to healthy controls (CN). Through the use of a reaction-time task, the study examines how various groups process multisensory and unisensory information. Participants are exposed to auditory (A), visual (V), and audiovisual (AV) stimuli and are asked to push a button as quickly as possible. Utilizing sophisticated decomposition techniques like Slice Tensor Component Analysis (SliceTCA) and Tucker decomposition, important neural components that differentiate people who perform better with multisensory stimuli ("integrators") from people who do not ("non-integrators") were extracted from the EEG data. These methods maintain the crucial brain dynamics associated with MSI while simplifying the high-dimensional EEG data. In addition to classifying integrators and non-integrators, a continuous analysis was performed using SliceTCA to examine the relationship between brain activity and response gain times—a measure of the speed advantage gained from multisensory stimuli compared to the faster unisensory condition. Beyond simple binary classification, this continuous analysis made it possible to understand MSI performance in more detail. The study found neural-behavior relationships by correlating neural components with response time gains. This finer-grained analysis highlighted how MSI impairments in these clinical populations manifest at both behavioral and neural levels. A key result of this study was the identification of P-300 playing a role in correlating neural activity with behavior. Moreover, EEGNet, a compact deep convolutional neural network, was utilized to distinguish between the ASD, SCZ, and CN groups since it is well-suited for extracting spatiotemporal features from EEG signals. Through utilization of the complete trial-by-trial EEG dataset, the model yielded further insights into the neural variations that underlie MSI. The general knowledge of how MSI is disrupted in people with ASD and SCZ has been aided by EEGNet's capacity to identify spatial and temporal variations in EEG data. All things considered, this work provides an extensive investigation of MSI through the integration of deep learning models and conventional decomposition methods. By highlighting notable variations in brain activity and behavior among ASD, SCZ, and CN groups, the study sheds light on the neural underpinnings of deficits in multisensory integration. Future diagnostic techniques and treatments targeted at enhancing sensory processing in neurodevelopmental disorders may benefit from these findings	en
heal.advisorName	Nikita, Konstantina	en
heal.committeeMemberName	Nikita, Konstantina	en
heal.committeeMemberName	Delis, Ioannis	en
heal.committeeMemberName	Golemati, Spyretta	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false