  As the number of automatic tools based on machine learning (ML) and
resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD)
detection keeps growing, the assessment of possible exacerbation of health
disparities by means of fairness and bias analysis becomes more relevant.
Protected attributes, such as gender, play an important role in PD diagnosis
development. However, analysis of sub-group populations stemming from different
genders is seldom taken into consideration in ML models' development or the
performance assessment for PD detection. In this work, we perform a systematic
analysis of the detection ability for gender sub-groups in a multi-center
setting of a previously developed ML algorithm based on power spectral density
(PSD) features of rs-EEG. We find significant differences in the PD detection
ability for males and females at testing time (80.5% vs. 63.7% accuracy) and
significantly higher activity for a set of parietal and frontal EEG channels
and frequency sub-bands for PD and non-PD males that might explain the
differences in the PD detection ability for the gender sub-groups.
