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arXiv · 2601.15731

FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging

Linyong Zou·Liang Zhang·Xiongfei Wang·Jia-Hong Gao·Yi Sun·Shurong Sheng·Kuntao Xiao·Wanli Yang·Pengfei Teng·Guoming Luan·Zhao Lv·Zikang Xu
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Abstract

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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Linyong Zou, Liang Zhang, Xiongfei Wang, Jia-Hong Gao, Yi Sun, Shurong Sheng, Kuntao Xiao, Wanli Yang, Pengfei Teng, Guoming Luan, Zhao Lv, Zikang Xu. 2026-01-22. FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging. https://arxiv.org/abs/2601.15731

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