Dwi Utari Surya, Sholeh Hadi Pramono, Panca Mudjirahardjo, Muhammad Aziz Muslim, Cries Avian, Mahdin Rohmatillah
Mental stress poses a significant global health burden, with rising prevalence linked to impaired cognitive functioning, reduced productivity, and increased neuropsychiatric risk. Electroencephalography (EEG) provides a non-invasive approach for capturing neural correlates of stress; however, effective classification remains challenging due to the complex spatiotemporal nature of brain activity. To address the limitations of current EEG-based stress classification methods, particularly their limited ability to jointly model temporal, spectral, and spatial dependencies. We propose a novel handcrafted feature-extraction framework, Spatiotemporal Connectivity and Fractal–Spectral Profile (STC-FSP). The framework integrates handcrafted descriptors from four domains, temporal, spectral, fractal, and spatial connectivity (including inter-channel correlation, hemispheric asymmetry, and phase-lag index) to capture both localized neural dynamics and inter-regional synchronization. To enhance cross-domain interaction and reduce redundancy, a log-transformed Pearson correlation is applied across the feature space. The proposed STC-FSP framework was evaluated on the publicly available DASPS dataset using multiple classical machine learning models. In binary classification, K-Nearest Neighbors (KNN) achieved the highest accuracy (93.18%), F1-score (93.89%), and AUC (93.89%), while in multiclass classification, XGBoost yielded competitive accuracy with an exceptionally low inference time of 0.0015 s, demonstrating real-time feasibility. Compared with existing EEG-based stress classification methods such as Baghdadi et al. (2019) (83.5% using Autoencoder), Syakiyla et al. (2023) (89.5% with S-LSMOTE), and Shikha et al. (2025) (NMI–RFE with Random Forest, 90.76%), the proposed STC-FSP achieves up to 16%–20% higher accuracy with significantly lower inference latency (~0.05s), confirming its superiority in both accuracy and computational efficiency. By fusing multi-domain descriptors and emphasizing low latency, the proposed approach is particularly suited to resource-constrained mental health monitoring applications. This study demonstrates the efficacy of interpretable handcrafted EEG features and log-correlation modelling for stress recognition. © 2013 IEEE.
Universitas Brawijaya, Electrical Engineering Department, Malang, 65145, Indonesia; Universitas Brawijaya, Creative and Digital Industry Department, Malang, 65145, Indonesia
