Abstract
Background Cognitive fatigue is a prevalent and disabling symptom in neurological and post-viral conditions, including multiple sclerosis (MS) and Long COVID. Assessment relies largely on self-report, and no validated objective biomarker exists, limiting reliable diagnosis and treatment monitoring. The aperiodic exponent of the Electroencephalogram (EEG) power spectrum, reflecting the excitation/inhibition (E/I) balance, is a promising candidate biomarker. We examined whether aperiodic exponent values can objectively identify pathological fatigue and assessed their classification accuracy.Methods We conducted a cross-sectional study, including 119 participants: 36 healthy controls, 33 with Long COVID-related fatigue (LCOF), and 50 with MS (23 fatigued and 27 nonfatigued). Resting-state EEGs were analyzed, and associations with fatigue ratings and group differences were assessed. Logistic mixed-effects regression models evaluated classification accuracy for fatigue status.Results Lower frontal aperiodic exponents were associated with higher cognitive fatigue across participants. Fatigued individuals, regardless of diagnosis, showed reduced frontal exponent values compared with nonfatigued groups, while no differences emerged in occipital regions. Logistic regression confirmed that frontal exponent values significantly predicted fatigue status, improving classification accuracy beyond age and depression, with good sensitivity and specificity.Conclusions The frontal aperiodic exponent is a regionally specific biomarker of cognitive fatigue across MS and LCOF. Mechanistic interpretation suggests an altered prefrontal E/I balance, which could inform the development of targeted interventions to alleviate cognitive fatigue. It offers a clinically accessible tool to complement self-report, support trial stratification, and enable objective treatment monitoring. Importantly, its presence across distinct disorders highlights its value as a transdiagnostic marker of fatigue.