ParViT: A modified Vision Transformer architecture for Parkinson’s Disease identification using EEG signals

Parkinson’s disease (PD) is a degenerative neurological condition that affects millions of individuals worldwide and is marked by both motor and non-motor indicators. In order to enhance patient outcomes through timely intervention, prompt detection and forecasting of PD is essential. Early detection of PD risk factors can help with the execution of management and treatment strategies that are beneficial in delaying the progression of the ailment. In this investigation, we present a novel hybrid framework intended for prediction of PD. The methodology begins by employing the Short-Time Fourier Transform (STFT) to process raw electroencephalography (EEG) data, facilitating the extraction of pertinent time-frequency characteristics. Subsequently, these features are organized into time-frequency blocks and inputted into an enhanced Vision Transformer(ViT) architecture, named ParViT, for classification of subjects into PD and Healthy Controls (HC). The proposed method demonstrates superior performance compared to current existing techniques in tasks related to PD identification, achieving an impressive accuracy rate of 98.25%, as well as precision, recall, specificity and F1 scores of 98.20%, 98.27%, 98.47% and 98.24%, respectively, as evidenced by experiments conducted on the publicly available UC San Diego dataset.