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

Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study