EEG functional connectivity for Parkinson's disease diagnosis and non-motor symptom prediction: a machine learning approach

Introduction

Parkinson's disease (PD) is associated with severe non-motor symptoms, including mood disorders and sleep disturbances, whose neural mechanisms are linked to dysfunction in large-scale brain networks. Functional connectivity measured by electroencephalography (EEG) can noninvasively map abnormalities at the network level.