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.
@article{arxiv.2601.15731,
title = {FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging},
author = {Linyong Zou and Liang Zhang and Xiongfei Wang and Jia-Hong Gao and Yi Sun and Shurong Sheng and Kuntao Xiao and Wanli Yang and Pengfei Teng and Guoming Luan and Zhao Lv and Zikang Xu},
journal= {arXiv preprint arXiv:2601.15731},
year = {2026}
}