An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging.

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Background: Normal aging involves gradual declines in brain function, whereas neurodegenerative disorders show accelerated deterioration. Electroencephalography (EEG) captures alterations in brain oscillatory activity, making it promising for detecting neurodegenerative disorders. However, existing approaches often emphasize classification performance over spectral biomarker identification, limiting clinical translation. Methods: We propose an EEG-based framework integrating explainable machine learning, analyses of inter-subject heterogeneity and temporal stability, and probabilistic modeling to identify reproducible spectral biomarkers of pathological brain aging. Two independent public EEG datasets comprising cognitively normal (CN) individuals and patients with Alzheimer's disease (AD), Parkinson's disease (PD), and frontotemporal dementia (FTD) were analyzed. Spectral features from canonical frequency bands were analyzed using SHAP to identify reproducible biomarkers in two datasets. The identified biomarker was subsequently characterized through inter-subject heterogeneity, temporal stability, and empirical distributions to assess disease-related alterations in oscillatory activity. Results: Several spectral features showed consistent disease-related associations across classification tasks. The θ/α ratio emerged as a representative biomarker, showing reduced temporal stability and broader empirical distributions in most neurodegenerative groups than in CN. Conclusions: Reduced temporal stability of reproducible EEG spectral biomarkers may represent an observable signature of disrupted oscillatory brain organization during pathological aging. The proposed framework characterizes a dimension of EEG spectral biomarkers that static summaries discard, offering an interpretable basis on which future longitudinal studies may build diagnostic and monitoring tools.

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