Advanced EEG signal processing techniques for cognitive classification in Parkinson's disease.

Abstract

 Parkinson's Disease (PD) is a neurodegenerative disorder that progressively deteriorates cognitive and motor abilities. It is imperative to detect cognitive impairment at an early stage, as it has a substantial effect on the quality of life of patients with Parkinson's disease. Electroencephalography (EEG) has the potential to detect cognitive decline by capturing brain activity, due to its non-invasive nature and high temporal resolution. Nevertheless, the complexity and cacophony present in EEG data necessitate the use of sophisticated processing methods to ensure precise analysis. This investigation investigates the most recent EEG signal processing methods for cognitive classification in Parkinson's disease, with an emphasis on time-frequency analysis, deep learning, and machine learning. Wavelet Transforms are among the techniques that offer detailed spectral and temporal insights, while Random Forest (RF) and Support Vector Machines (SVM) models facilitate effective classification. Additionally, the accuracy of feature extraction and classification is improved by Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This research emphasizes the potential of these methodologies in the early diagnosis, personalized treatment, and continuous monitoring of PD patients. The significance of surmounting obstacles such as chaotic data and restricted EEG datasets to enhance clinical outcomes through precise cognitive assessment is underscored by the study