Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation

In recent years, Electroencephalographic (EEG) analysis has gained prominence in stress research when combined with AI and Machine Learning (ML) models for validation. In this study, a lightweight dynamic brain connectivity framework for estimating Time-Varying Directed Transfer Function (TV-DTF) is proposed to capture dynamic effective connectivity in EEG signals. TV-DTF estimates the directional information flow between EEG channels across distinct frequency bands, thereby capturing temporal and directed influences that are often overlooked by static functional connectivity measures. EEG recordings from the 32-channel SAM 40 dataset were employed, focusing on mental arithmetic task trials. The discriminative capability of the dynamic EEG-based TV-DTF features was validated through ML -based experiments utilizing Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Experimental results show that alpha-TV-DTF provides the strongest discriminative power, with SVM achieving 89.73% accuracy in 3-class classification and with XGBoost achieving 93.69% accuracy in 2-class classification. Relative to absolute power and phase locking-based functional connectivity features, alpha-TV-DTF and beta-TV-DTF achieved higher performance across the ML models, highlighting the advantages of dynamic over static measures. Feature importance analysis further highlighted prominent frontal-parietal and frontal-occipital informational influences, emphasizing the regulatory role of frontal regions under stress. These findings validate the lightweight TV-DTF as a robust framework, revealing spatiotemporal brain dynamics and directional influences across different stress levels.