FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914272638926848 |
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| author | Zou, Linyong Zhang, Liang Wang, Xiongfei Gao, Jia-Hong Sun, Yi Sheng, Shurong Xiao, Kuntao Yang, Wanli Teng, Pengfei Luan, Guoming Lv, Zhao Xu, Zikang |
| author_facet | Zou, Linyong Zhang, Liang Wang, Xiongfei Gao, Jia-Hong Sun, Yi Sheng, Shurong Xiao, Kuntao Yang, Wanli Teng, Pengfei Luan, Guoming Lv, Zhao Xu, Zikang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging Zou, Linyong Zhang, Liang Wang, Xiongfei Gao, Jia-Hong Sun, Yi Sheng, Shurong Xiao, Kuntao Yang, Wanli Teng, Pengfei Luan, Guoming Lv, Zhao Xu, Zikang Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.15731 |