自适应皮层约束的脑电-视觉对齐用于零样本脑到图像检索
Adaptive Cortically Constrained EEG-Vision Alignment for Zero-Shot Brain-to-Image Retrieval
- Chongqing University of Posts and Telecommunications（重庆邮电大学）
- Shanghai Jiaotong University（上海交通大学）
- Chongqing Normal University（重庆师范大学）
- South China University of Technology（华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对零样本脑到图像检索，提出自适应皮层约束脑电-视觉对齐方法，通过ROI级源模式表征与证据加权监督，在THINGS-EEG上实现强200路检索性能。
AI中文摘要：
零样本脑到图像检索需要在嘈杂的脑电（EEG）响应与视觉表征之间建立稳健的对齐。现有的脑电-视觉对齐方法通常在传感器空间操作，并对所有响应施加固定的视觉监督，忽略了头皮脑电中的空间混合以及对齐可靠性中的响应间变异性。我们提出了一种用于零样本脑到图像检索的自适应皮层约束脑电-视觉对齐方法。该方法将脑电响应重构为预定义的感兴趣区域（ROI）级源模式表征，并使用神经-ROI注意力编码器（Neuro-ROI Attention Encoder）对其进行编码。为了处理响应间变异性，我们引入了一种基于证据的自适应视觉监督策略，该策略利用基于模型的对齐证据对细节控制的视觉目标进行加权。在THINGS-EEG数据集上，所提出的方法实现了强大的200路零样本检索性能，其中ROI级归因提供了对所学源模式表征的事后可解释性。这些结果表明，皮层约束的表征学习和自适应监督可以共同支持用于零样本脑到图像检索的脑电-视觉对齐。
英文摘要：
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.
