Adaptive Cortically Constrained EEG-Vision Alignment for Zero-Shot Brain-to-Image Retrieval

2026-09-21

AI Summary

本文提出一种自适应皮层约束的EEG-视觉对齐方法，通过重建EEG响应并采用基于证据的自适应视觉监督策略，解决零样本脑-图像检索中信号噪声和响应变异性问题。

Abstract

Zero-shot brain-to-image retrieval requires robust alignment between noisy EEG responses and visual representations. Existing EEG-vision alignment methods often operate in sensor space and apply fixed visual supervision to all responses, ignoring both spatial mixing in scalp EEG and response-wise variability in alignment reliability. We propose an adaptive cortically constrained EEG-vision alignment method for zero-shot brain-to-image retrieval. The method reconstructs EEG responses into predefined ROI-level source-pattern representations and encodes them with a Neuro-ROI Attention Encoder. To handle response-wise variability, we introduce an evidence-based adaptive visual supervision strategy that weights detail-controlled visual targets using model-based alignment evidence. On THINGS-EEG, the proposed method achieves strong 200-way zero-shot retrieval performance, with ROI-level attribution providing post hoc interpretability of the learned source-pattern representations. These results show that cortically constrained representation learning and adaptive supervision can jointly support EEG-vision alignment for zero-shot brain-to-image retrieval.

Problem

• zero-shot brain-to-image retrieval
• EEG-vision alignment
• spatial mixing
• response-wise variability

Innovation

• adaptive cortically constrained alignment
• ROI-level source-pattern representation
• Neuro-ROI Attention Encoder
• evidence-based adaptive visual supervision
• zero-shot brain-to-image retrieval