Spatial–Temporal Connectivity Descriptors for EEG-Based Mental Stress Detection Using Interpretable Machine Learning

This is the authors' abstract. We don't add key points for this paper.

Mental stress is a massive threat to global health because it has serious adverse effects on people's memory, emotional well-being, and productivity. Although electroencephalography (EEG) offers a non-invasive, high-temporal-resolution modality for stress assessment, the complex, non-stationary nature of stress-related neural responses remains a significant challenge for reliable EEG-based classification systems. The Research contribution is the development of an interpretable and modular EEG-based stress recognition framework that systematically integrates spatial–temporal connectivity descriptors with classical machine learning models. The proposed framework extracts handcrafted features from two complementary domains: temporal descriptors, including time-domain statistics and Hjorth parameters, and spatial connectivity measures, including inter-channel Pearson correlation, amplitude asymmetry, and Phase Lag Index (PLI). In addition, a log-transformed meta-correlation descriptor is introduced to capture nonlinear dependencies among temporal features. EEG signals from the DASPS dataset are segmented and evaluated using stratified 5-fold cross-validation across multiple classical classifiers. Experimental results demonstrate that the proposed framework achieves 99.18% accuracy and 99.89% AUC in binary stress classification using KNN, and 98.56% accuracy and 99.95% AUC in multiclass classification using SVM. Furthermore, XGBoost provides a favorable balance between accuracy and computational efficiency, achieving low inference latency (0.0015 s). These results confirm consistent performance across classifiers without reliance on deep learning architectures. Overall, the proposed framework offers an interpretable, low-latency solution for EEG-based mental stress detection, with strong potential for deployment in practical applications, such as wearable mental health monitoring systems and real-time stress assessment platforms.