GEPD:GAN-Enhanced Generalizable Model for EEG-Based Detection of Parkinson's Disease

TL;DR

This paper introduces GEPD, a GAN-enhanced model that improves cross-dataset EEG-based Parkinson's disease detection by generating high-quality data and using a robust CNN architecture, achieving state-of-the-art accuracy.

Contribution

The paper presents a novel GAN-based data augmentation and a multi-CNN classification framework for improved generalization across EEG datasets in Parkinson's detection.

Findings

Achieved 84.3% accuracy in cross-dataset Parkinson's detection.

Demonstrated comparable performance to state-of-the-art models.

Validated the effectiveness of GAN-generated data for EEG classification.

Abstract

Electroencephalography has been established as an effective method for detecting Parkinson's disease, typically diagnosed early.Current Parkinson's disease detection methods have shown significant success within individual datasets, however, the variability in detection methods across different EEG datasets and the small size of each dataset pose challenges for training a generalizable model for cross-dataset scenarios. To address these issues, this paper proposes a GAN-enhanced generalizable model, named GEPD, specifically for EEG-based cross-dataset classification of Parkinson's disease.First, we design a generative network that creates fusion EEG data by controlling the distribution similarity between generated data and real data.In addition, an EEG signal quality assessment model is designed to ensure the quality of generated data great.Second, we design a classification network that utilizes a combination of multiple convolutional neural networks to effectively capture the time-frequency characteristics of EEG signals, while maintaining a generalizable structure and ensuring easy convergence.This work is dedicated to utilizing intelligent methods to study pathological manifestations, aiming to facilitate the diagnosis and monitoring of neurological diseases.The evaluation results demonstrate that our model performs comparably to state-of-the-art models in cross-dataset settings, achieving an accuracy of 84.3% and an F1-score of 84.0%, showcasing the generalizability of the proposed model.