Decoding speech perception utilizing EEG-Based partial information decomposition and hierarchical drift diffusion modeling

Kalaitzidou, Parthena; Καλαϊτζίδου Παρθένα

dc.contributor.author	Kalaitzidou, Parthena	en
dc.contributor.author	Καλαϊτζίδου Παρθένα	el
dc.date.accessioned	2025-06-12T10:49:26Z
dc.date.available	2025-06-12T10:49:26Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62052
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29748
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	Speech Perception	en
dc.subject	Evidence Accumulation	en
dc.subject	Redundant Information	en
dc.subject	Synergistic Information	en
dc.subject	Unique Information	en
dc.subject	Cognitive Load	en
dc.subject	Hierarchical Drift Diffusion Modeling (HDDM)	en
dc.subject	Partial Information Decomposition (PID)	en
dc.subject	Neural Encoding	en
dc.subject	Decision-Making	en
dc.title	Decoding speech perception utilizing EEG-Based partial information decomposition and hierarchical drift diffusion modeling	en
heal.type	masterThesis
heal.classification	Biomedical Engineering	en
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-30
heal.abstract	This study investigates the neural and cognitive mechanisms underlying speech perception and decision-making under varying task difficulties. By combining Partial Information Decomposition (PID) and Hierarchical Drift Diffusion Modeling (HDDM), we explored how unique, synergistic, and redundant information encoded in neural activity influences key decision-making parameters such as drift rate, decision boundary, and non-decision time. EEG data from 22 participants were analyzed during a speech perception task involving rhyming and non-rhyming stimuli, categorized into easy and hard conditions. PID analysis revealed task-specific differences in neural encoding across Delta (4 Hz), Theta (7 Hz), and Broadband (10 Hz) frequency bands. Hard tasks elicited higher cognitive load, with increased unique and synergistic information in Delta and Theta bands, especially in fronto-central and parietal regions. Theta waves played a prominent role in easier tasks, facilitating efficient processing of unique and synergistic information. HDDM analysis showed that drift rate increased with higher unique or synergistic neural information, indicating faster evidence accumulation in congruent trials. Non-decision times were shorter during trials with high synergy, reflecting faster sensory encoding, while incongruent trials with higher redundancy led to longer non-decision times. Decision boundary parameters remained consistent across conditions, suggesting stable decision thresholds. Additionally, choice switching significantly impacted reaction times and non-decision times, with faster responses and lower non-decision times observed during correct switches, emphasizing the neural basis of adaptive behavior. These findings highlight the dynamic interplay between neural encoding, decision-making, and cognitive load. By integrating PID and HDDM, this study provides novel insights into how the brain processes sensory and motor information to adapt decision-making strategies during speech perception.	en
heal.advisorName	Κωνσταντίνα, Νικήτα	el
heal.committeeMemberName	Δελής, Ιωάννης	el
heal.committeeMemberName	Βουλόδημος, Αθανάσιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	79 σ.	el
heal.fullTextAvailability	false