Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.