Abstract
In this study, we investigated whether individual brain maps of natural frequencies derived from EEG can serve as physiologically meaningful biomarkers for Parkinson’s disease (PD), a disorder characterized by altered oscillatory activity. Data were gathered from three independent, openly available EEG databases. Natural frequency maps were extracted for 57 PD participants and 57 sex- and age-matched healthy controls (HC). The group-level brain maps showed region-specific slowing of oscillatory activity in PD, particularly in frontomedial and left frontolateral cortices, as well as hemispheric asymmetries consistent with the lateralized onset of motor symptoms. A subset of 100 participants (50 PD, 50 matched controls) was then used to train and internally validate three machine learning classifiers—support vector machine (SVM), random forest, and logistic regression—under different PCA-based dimensionality reduction and feature normalization schemes. Among all models, SVM achieved the best performance (AUC ≈ 77%). This classifier was subsequently retrained on the full set of 100 participants and evaluated on an independent hold-out test set of 14 participants, achieving an AUC of 75.5% with 71.4% accuracy. Feature contribution analysis highlighted the regions that were most informative for classification toward each class, complementing the group-level observations. Overall, these results indicate that natural frequency mapping captures disease-related alterations in cortical dynamics and provides interpretable features for EEG-based diagnostic tools, opening new avenues for biomarker development in oscillopathies.
Authors
Arana, L., Gross, J., & Capilla, A.
https://doi.org/10.64898/2025.12.09.25341882