EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings

In real-world occupational settings, mental fatigue commonly emerges from the combination of sleep deprivation with prolonged cognitive and physical workload. However, this multidimensional fatigue profile is rarely captured in controlled experimental paradigms that examine brain activation and fatigue-related responses. Consequently, the validity and transferability of cognitive fatigue biomarkers identified in passive, laboratory-based fatigue paradigms to real-world active occupational conditions remain largely unexplored. This work investigates the functional connectivity of resting-state EEG in an active (real-world) and a passive (lab-induced) mental fatigue dataset, employing a multilayer graph representation in sensor-space that integrates both within-band and cross-frequency connectivity information. Specifically, functional connections within five EEG frequency bands are quantified using Phase Lag Index (PLI), while cross-frequency interactions between bands are characterized using Phase-Amplitude Coupling (PAC). Subsequently, a dataset-specific feature extraction pipeline is adopted, utilizing multiple machine learning classifiers to co-validate the most discriminative features, while the resulting network topologies are interpreted in light of established mental fatigue neurophysiological mechanisms. Our results promote the inter-frequency PAC metric as more informative for fatigue discrimination in both datasets, compared to the intra-frequency PLI, with smaller feature sets needed in the "active" vs. "passive" fatigue setting for information saturation. The topology of the selected feature sets indicates a reorganization of neural resources that is strictly dependent on the nature of fatigue induction.