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Dynamic Functional Connectivity Patterns in Resting-State EEG for Classifying Learning Strategies
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 64 |
| Year of Publication: 2025 |
| Authors: Si Thu Aung, Khin Muyar Kyaw, Kyaw Kyaw Oo, Aung Cho Oo, Kyawt Kyawt Zin, Nei Rin Zara Lwin, Thura Tun |
10.5120/ijca2025926070
|
Si Thu Aung, Khin Muyar Kyaw, Kyaw Kyaw Oo, Aung Cho Oo, Kyawt Kyawt Zin, Nei Rin Zara Lwin, Thura Tun . Dynamic Functional Connectivity Patterns in Resting-State EEG for Classifying Learning Strategies. International Journal of Computer Applications. 187, 64 ( Dec 2025), 10-13. DOI=10.5120/ijca2025926070
Abstract
Dynamic functional connectivity (dFC) captures temporal variations in brain network interactions, offering deeper insights into cognitive processes compared to static connectivity measures. This study proposes a novel framework for classifying different learning strategies—control, active, and passive—using resting-state electroencephalography (EEG). Resting-state EEG data from twenty-one participants were preprocessed and analyzed using the Phase Lag Index (PLI) to compute functional connectivity across 18 EEG channels. Dynamic connectivity matrices were generated using sliding-window correlations, and their upper-triangular elements were vectorized to obtain subject-specific dFC features. Euclidean distance and multidimensional scaling (MDS) were applied for dimensionality reduction before classification. Statistical analyses, including paired and Welch’s t-tests with Bonferroni correction, revealed significant within- and between-group differences (p < 10⁻⁸). Machine learning models—K-Nearest Neighbors (KNN) and Random Forest (RF)—achieved classification accuracies exceeding 80% and 70%, respectively, in distinguishing both within- and between-group patterns. These findings demonstrate that dFC features from resting-state EEG can effectively differentiate learning strategies, reflecting distinct neural reorganization patterns associated with cognitive engagement. The proposed framework provides a foundation for exploring EEG-based biomarkers of cognitive processes and potential applications in educational neuroscience and clinical diagnostics.
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