A Novel EEG-Based Parkinson's Disease Detection Model Using Multiscale Convolutional Prototype Networks

Objective and accurate detection of Parkinson's disease (PD) is crucial for timely intervention and treatment. Electroencephalography (EEG) has been proven to characterize PD by measuring brain activity. In recent years, deep learning methods have gained great attention in automated PD detection, but their performance is limited by insufficient data samples. In this article, we propose a novel PD automated detection model named the multiscale convolutional prototype network (MCPNet), which integrates and improves upon multiscale convolutional neural networks (CNNs) and prototype learning. On the one hand, it employs multiscale CNNs to extract brain features from different scales, enhancing feature diversity and utilization. On the other hand, a prototype calibration strategy is introduced to mitigate the effect of data noise on prototype generation, improving the generalization performance of model. Multiple within-dataset and cross-dataset experiments on three different datasets demonstrate the effectiveness of our model in PD detection. The leave-one-subject-out (LOSO) results of within-dataset experiments show that MCPNet achieves an accuracy of 92.5%, a sensitivity of 93.1%, a specificity of 91.9%, and an AUC of 92.4% in cross-subject classification between PD patients and healthy controls. In the cross-dataset classification, the performance of MCPNet is somewhat weakened due to dataset variations. However, this weakening is partially compensated by introducing the prototype calibration strategy. With the introduction of the calibration strategy, the accuracy of cross-dataset classification increases to 90.2%, a 4.0% improvement compared to when it is not used. These results indicate that the proposed model may be a promising tool for automated PD diagnosis.