  Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's
disease (PD) diagnosis. In particular, the power spectral density (PSD) of
low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha}
and \b{eta}) has been shown to be significantly different in patients with PD
as compared to subjects without PD (non-PD). However, rs-EEG feature extraction
and the interpretation thereof can be time-intensive and prone to examiner
variability. Machine learning (ML) has the potential to automatize the analysis
of rs-EEG recordings and provides a supportive tool for clinicians to ease
their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD
subjects pooled from four datasets obtained at different centers. We propose an
end-to-end pipeline consisting of preprocessing, extraction of PSD features
from clinically validated frequency bands, and feature selection before
evaluating the classification ability of the features via ML algorithms to
stratify between PD and non-PD subjects. Further, we evaluate the effect of
feature harmonization, given the multi-center nature of the datasets. Our
validation results show, on average, an improvement in PD detection ability
(69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features
and performing univariate feature selection (k = 202 features). Our final
results show an average global accuracy of 72.2% with balanced accuracy results
for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%,
respectively.
