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

arXiv: https://arxiv.org/abs/2508.14074 · HTML: https://mlsanity.com/paper/2508.14074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX](https://mlsanity.com/paper/2207.12369/) (2022)
- [A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction](https://mlsanity.com/paper/2012.00430/) (2020)
- [Deep learning with convolutional neural networks for decoding and visualization of EEG pathology](https://mlsanity.com/paper/1708.08012/) (2017)

## Built on by (most cited first, 1 of 1)

- [Channel Selected Stratified Nested Cross Validation for Clinically Relevant EEG Based Parkinsons Disease Detection](https://mlsanity.com/paper/2601.05276/) (2025)

## Abstract (arXiv)

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.