提出自适应特征精炼框架，提升脑电源成像精度
FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging
- 多视角自适应精炼频谱、时序与局部特征
- 在仿真与真实数据上显著提升定位准确率
- 适合脑疾病诊断与脑功能研究者使用
脑电源成像（ESI）是诊断脑部疾病的重要技术。尽管基于模型优化和深度学习的方法已取得良好效果，但特征的精准选择与精炼仍是实现高精度 ESI 的核心挑战。本文提出 FAIR-ESI 框架，通过三种自适应机制实现特征重要性精炼：基于 FFT 的频谱特征精炼、加权时序特征精炼，以及基于自注意力的块级特征精炼。在两个具有多样化配置的仿真数据集及两个真实临床数据集上的大量实验验证了该框架的有效性，展现出其在推动脑疾病诊断和深化脑功能理解方面的潜力。
原文摘要 · Abstract (English)
An essential technique for diagnosing brain disorders is electrophysiological source imaging (ESI). While model-based optimization and deep learning methods have achieved promising results in this field, the accurate selection and refinement of features remains a central challenge for precise ESI. This paper proposes FAIR-ESI, a novel framework that adaptively refines feature importance across different views, including FFT-based spectral feature refinement, weighted temporal feature refinement, and self-attention-based patch-wise feature refinement. Extensive experiments on two simulation datasets with diverse configurations and two real-world clinical datasets validate our framework's efficacy, highlighting its potential to advance brain disorder diagnosis and offer new insights into brain function.
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