EEG Data Classification for Early-Stage Detection of Parkinson’s Disease Using Deep Learning: A Survey

Introduction Parkinson’s Disease (PD) is a neurodegenerative progressive disorder that affects movement and cognitive function. Electroencephalography (EEG) has recently proven to be a helpful tool for the non-invasive diagnosis of PD, particularly in its early stages. The deep learning revolution where significant improvements have been observed in the classification of electroencephalogram(EEG) signals that potentially uncover subtle distinctions in brain electrical activity caused by initial-stage PD. In this survey, we examine recent work in the field of EEG-based deception detection, discussing EEG signal preprocessing, feature extraction, and deep learning techniques including CNNs, RNNs, and hybrid methods. The challenges, including data availability, noise, model interpretability, etc., they face, as well as solutions for these challenges, are presented. This study also discusses the future research directions you can take to improve the accuracy and robustness of the detection of PD.