This study explores the electrophysiological mechanisms underlying motor and non-motor fluctuations (NMF) in Parkinson's disease (PD), focusing on cortical and subthalamic dynamics during acute dopaminergic stimulation. PD is characterized by both motor symptoms and disabling non-motor symptoms-including neuropsychiatric fluctuations that remain poorly understood. While local field potentials (LFP) recorded from the subthalamic nucleus (STN) via deep brain stimulation (DBS) have revealed beta-band abnormalities linked to motor dysfunction, little is known about the oscillatory signatures of NMF. Preliminary data from our group suggested that gamma-band EEG activity in frontotemporal regions may correlate with neuropsychiatric fluctuations. This Swiss, two-center, prospective observational study aims to investigate resting-state electroencephalogram (EEG) and STN-LFP correlates of motor and non-motor symptoms during a modified levodopa challenge in 30 PD patients with STN-DBS. Using high-density EEG and chronically implanted Medtronic Percept™ DBS devices, electrophysiological data will be collected across five clinical states (combinations of ON/OFF levodopa and DBS). Clinical symptoms will be assessed alongside electrophysiological activity to identify frequency-specific cortical-STN biomarkers. Machine learning models (e.g., LASSO regression) will be used to predict motor and non-motor states from EEG and LFP data, enabling the identification of dynamic oscillatory markers. This could inform future adaptive DBS strategies. The study leverages advanced methods in neurophysiology, imaging, and machine learning to deepen our understanding of PD fluctuations. It also proposes the first detailed electrophysiological mapping of NMF, which could improve patient stratification and neuromodulation therapies. Anatomical validation of DBS lead placement will be performed using standard neuroimaging toolkits.