Machine Learning-Based Comparison of EEG Signals During Associative Equivalence Learning with Visual Stimuli of Varying Complexity

Kiss Ádám
Machine Learning-Based Comparison of EEG Signals During Associative Equivalence Learning with Visual Stimuli of Varying Complexity.
Doctoral thesis (PhD), University of Szeged.
(2025)

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Abstract in foreign language

Associative learning is a fundamental cognitive process that enables individuals to form connections between stimuli and responses. This thesis investigates the neural mechanisms underlying associative learning using electroencephalography (EEG) and machine learning techniques. The study compares two tasks: the original Rutgers Acquired Equivalence Test (RAET) and a simplified, feature-reduced version called Polygon. By analyzing cortical activity, the research aims to understand how stimulus complexity and verbalizability influence learning processes. EEG signals were recorded from participants performing both tasks and preprocessed using Independent Component Analysis (ICA) to isolate neural components. Machine learning classifiers, including Long Short-Term Memory (LSTM) networks and Support Vector Classification (SVC), were employed to detect differences in brain activity between the two tasks. The analysis focused on distinct brain regions, such as the frontal, temporal, occipital, and parietal lobes, to assess their roles in learning, attention, and decision-making. Key findings reveal that the frontal region shows the most significant differences between the RAET and Polygon tasks. These differences are linked to varying levels of attention, memory load, and decision-making processes influenced by stimulus complexity. In contrast, the parietal region exhibited minimal variation, suggesting its role is less affected by stimulus features. Although limited in sample size, this study demonstrates the effectiveness of combining EEG with machine learning to uncover cortical activity patterns in associative learning. The findings provide a foundation for future research and highlight the potential of these methods to advance our understanding of cognitive processes.

Item Type: Thesis (Doctoral thesis (PhD))
Creators: Kiss Ádám
Hungarian title: Eltérő összetettségű képi asszociációs egyenértékűségi tanulások EEG jeleinek összehasonlítása gépi tanulással
Supervisor(s):
Supervisor
Position, academic title, institution
MTMT author ID
Nagy Attila
egyetemi oktató, PhD habil, SZTE SZAOK Élettani Intézet
10012540
Kelemen András
egyetemi oktató, PhD, SZTE JGYPK, TTIK
10026888
Subjects: 03. Medical and health sciences > 03.01. Basic medicine > 03.01.05. Neurosciences (including psychophysiology) > 03.01.05.01. Neuroimaging and computational neuroscience
Divisions: Doctoral School of Theoretical Medicine
Discipline: Medicine > Theoretical Medicine
Language: English
Date: 2025
Number of Pages: 66
Uncontrolled Keywords: associative learning, human, visual, stimulus complexity, verbalizability, electrophysiology, independent component analysis, artificial intelligence, EEG
Item ID: 12478
Date Deposited: 2025. Feb. 26. 17:54
Last Modified: 2025. Mar. 04. 10:54
URI: https://doktori.bibl.u-szeged.hu/id/eprint/12478
Defence/Citable status: Not Defended. (Do not cite until it has not assigned DOI number!)

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