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

TL;DR

FAIR-ESI introduces an adaptive feature refinement framework for electrophysiological source imaging, improving accuracy in brain disorder diagnosis through spectral, temporal, and patch-wise feature enhancements validated on simulations and clinical data.

Contribution

The paper presents a novel adaptive feature importance refinement framework for ESI, integrating spectral, temporal, and self-attention-based methods to enhance source imaging accuracy.

Findings

Validated on simulation datasets with diverse configurations

Demonstrated effectiveness on real-world clinical datasets

Potential to improve brain disorder diagnosis accuracy

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.