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Found 2 papers, 1 papers with code
Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study
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.
Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study
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).
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