用深度学习分析脑电图，提升神经疾病诊断精准度。
Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics
- 整合46个数据集，系统评估7类神经疾病诊断方法。
- 预训练多任务模型显著提升模型泛化能力与可迁移性。
- 提出标准化基准，推动脑电诊断研究可复现与实用化。
神经系统疾病带来重大全球健康挑战，推动脑信号分析技术发展。头皮脑电图（EEG）和颅内脑电图（iEEG）广泛用于诊断与监测。然而，数据集异质性和任务差异制约了鲁棒深度学习方案的开发。本文系统综述了基于EEG/iEEG的神经疾病诊断中深度学习的最新进展，涵盖7种神经系统疾病及46个数据集。针对每种疾病，梳理代表性方法及其量化结果，整合性能对比、数据使用模式、模型设计与任务适配分析，并强调预训练多任务模型在实现可扩展、通用解决方案中的关键作用。最后，提出标准化基准以跨数据集评估模型表现，提升研究可复现性，凸显近期创新正推动神经疾病诊断迈向智能化、自适应的医疗体系。
原文摘要 · Abstract (English)
Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. For each condition, we review representative methods and their quantitative results, integrating performance comparisons with analyses of data usage, model design, and task-specific adaptations, while highlighting the role of pre-trained multi-task models in achieving scalable, generalizable solutions. Finally, we propose a standardized benchmark to evaluate models across diverse datasets and improve reproducibility, emphasizing how recent innovations are transforming neurological diagnostics toward intelligent, adaptable healthcare systems.
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