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Parkinsons disease (PD) diagnosis is challenging due to subtle early clinicalsigns. F-DOPA PET is commonly used for early PD diagnosis. We explore thepotential of machine-learning (ML) based EEG features extracted fromsingle-channel EEG during auditory cognitive assessment as a noninvasive,low-cost support for PD diagnosis. The study included data collected from 32participants who underwent an F-DOPA PET scan as part of their standardtreatment and 20 cognitively healthy controls. Participants performed anauditory cognitive assessment recorded with Neurosteer EEG device. Dataprocessing involved wavelet-packet decomposition and ML. First, a predictionmodel was developed to predict 1/3 of the undisclosed F-DOPA results. Then,generalized linear mixed models were calculated to distinguish between PD andnon-PD subjects on the frequency bands and ML-based EEG features (A0 and L1)previously associated with cognitive functions. The prediction model accuratelylabeled patients with unrevealed scores as positive F-DOPA. Novel EEG featureA0 and the Delta band showed significant separation between study groups, withhealthy controls exhibiting higher activity than PD patients. EEG feature L1activity was significantly lower in resting state compared to high-cognitiveload. This effect was absent in the PD group, suggesting that lower activity inresting state is lacking in PD patients. This study successfully demonstratedthe ability to separate patients with positive vs. negative F-DOPA PET resultswith an easy-to-use single-channel EEG during an auditory cognitive assessment.Future longitudinal studies should further explore the potential utility ofthis tool for early PD diagnosis and as a potential biomarker in PD.

Evaluation of Parkinsons disease with early diagnosis using single-channel EEG features and auditory cognitive assessment