A Swap-Adversarial Framework for Improving Domain Generalization in Electroencephalography-Based Parkinson's Disease Prediction

2026-02-11

AI Summary

This study addresses the challenges of poor reproducibility, high inter-subject variability, and the high-dimensional small-sample problem in EEG-based Parkinson’s disease prediction, which stem from ethical constraints and the lack of publicly available benchmarks. To this end, we construct the first reproducible ECoG benchmark dataset based on the 6-OHDA rat model and propose a Swap-Adversarial framework. This framework introduces an Inter-Subject Balanced Channel Swapping (ISBCS) mechanism to mitigate individual differences and integrates domain-adversarial training to extract task-relevant features, significantly enhancing generalization across subjects, sessions, and modalities (ECoG/EEG). Experiments demonstrate that our method consistently outperforms baseline approaches across diverse cross-domain settings, exhibiting robust performance particularly under high variability, and achieves strong cross-modal transferability on public EEG datasets.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Electroencephalography (ECoG) offers a promising alternative to conventional electrocorticography (EEG) for the early prediction of Parkinson's disease (PD), providing higher spatial resolution and a broader frequency range. However, reproducible comparisons has been limited by ethical constraints in human studies and the lack of open benchmark datasets. To address this gap, we introduce a new dataset, the first reproducible benchmark for PD prediction. It is constructed from long-term ECoG recordings of 6-hydroxydopamine (6-OHDA)-induced rat models and annotated with neural responses measured before and after electrical stimulation. In addition, we propose a Swap-Adversarial Framework (SAF) that mitigates high inter-subject variability and the high-dimensional low-sample-size (HDLSS) problem in ECoG data, while achieving robust domain generalization across ECoG and EEG-based Brain-Computer Interface (BCI) datasets. The framework integrates (1) robust preprocessing, (2) Inter-Subject Balanced Channel Swap (ISBCS) for cross-subject augmentation, and (3) domain-adversarial training to suppress subject-specific bias. ISBCS randomly swaps channels between subjects to reduce inter-subject variability, and domain-adversarial training jointly encourages the model to learn task-relevant shared features. We validated the effectiveness of the proposed method through extensive experiments under cross-subject, cross-session, and cross-dataset settings. Our method consistently outperformed all baselines across all settings, showing the most significant improvements in highly variable environments. Furthermore, the proposed method achieved superior cross-dataset performance between public EEG benchmarks, demonstrating strong generalization capability not only within ECoG but to EEG data. The new dataset and source code will be made publicly available upon publication.

Problem

• Domain Generalization
• Parkinson's Disease Prediction
• Electrocorticography (ECoG)
• Inter-subject Variability
• High-dimensional Low-sample-size (HDLSS)

Innovation

• Swap-Adversarial Framework
• Domain Generalization
• Inter-Subject Variability
• ECoG-based PD Prediction
• Cross-dataset Transfer