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.

翻译：电生理源成像（ESI）是诊断脑部疾病的一项关键技术。尽管基于模型的优化方法和深度学习方法在该领域已取得有前景的结果，但特征的准确选择与优化仍是实现精确ESI的核心挑战。本文提出FAIR-ESI，一种新颖的框架，能够自适应地优化多视角下的特征重要性，包括基于FFT的谱特征优化、加权时域特征优化以及基于自注意力的分块特征优化。在两个不同配置的仿真数据集和两个真实世界临床数据集上的大量实验验证了我们框架的有效性，突显了其在推动脑部疾病诊断和提供脑功能新见解方面的潜力。