Fair-Esi: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging

2025-12-15

IEEE International Conference on Bioinformatics and Biomedicine

AI Summary

This study addresses the limitations in electrophysiological source imaging (ESI) caused by inaccurate feature selection and refinement, which hinder precise diagnosis of brain disorders. To overcome this, we propose FAIR-ESI, a novel framework that introduces, for the first time in ESI, a cross-view adaptive feature importance refinement mechanism integrating spectral, temporal, and local spatial information. Specifically, FAIR-ESI dynamically optimizes multi-view feature representations through FFT-based spectral refinement, weighted temporal refinement, and self-attention-driven local patch refinement. Extensive experiments on two simulated and two real-world clinical datasets demonstrate that the proposed method significantly improves source localization accuracy, offering a new paradigm for high-precision mapping of brain function.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

An essential technique for diagnosing brain disorders is electrophysiological source imaging (ESI). While modelbased 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 FFTbased 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.

Problem

• electrophysiological source imaging
• feature selection
• feature refinement
• brain disorders
• ESI

Innovation

• Feature Adaptive Refinement
• Electrophysiological Source Imaging
• Self-Attention Mechanism
• Spectral-Temporal Feature Fusion
• Deep Learning for ESI