EEG Biomarkers for Stress Detection: A Comparative Analysis of Hjorth Parameters, Spectral and Fractal-Dimension Measures

EEG-based stress detection is increasingly relevant for intelligent sensing and human–computer interaction, particularly for systems that aim to adapt to users’ changing cognitive and affective states. While conventional EEG-based stress detection approaches mainly rely on spectral band-power features, fractal-dimension measures and Hjorth parameters may provide additional information about underlying brain dynamics. However, their relative contribution, spatial distribution, and frequency-band dependence remain insufficiently understood. In this study, pre-recorded EEG data from a stress-inducing experiment were analyzed using statistical feature pre-selection and multiple machine-learning classifiers. The contribution of different EEG features was evaluated across participants, while scalp topographical maps were used to characterize the spatial distribution of informative features. The results identify Hjorth activity, frontal $\beta$ asymmetry, and the $\gamma /\theta$ ratio as among the most frequently selected features in the ANOVA-based feature-selection analysis. Combined feature-space configurations including Hjorth parameters and fractal-dimension measures achieved higher classification performance than the conventional-feature reference configuration. These findings provide further insight into the spatial and frequency-specific characteristics of EEG features associated with the investigated stress-inducing cognitive task and demonstrate the value of combining conventional spectral features with Hjorth parameters and fractal-dimension measures for physiological-state sensing. The proposed approach may support the development of computationally efficient EEG-based monitoring of stress-related cognitive states for intelligent and adaptive human–computer interaction, including neuroadaptive and context-aware systems.