使EEG能看见：用于脑-视觉匹配的结构表示
Aligning What EEG Can See: Structural Representations for Brain-Vision Matching
AI总结：
本文提出EEG-可见层选择策略和分层互补融合框架，通过将EEG信号与中间视觉层对齐，解决跨模态信息不匹配问题，提升零样本视觉解码性能。
摘要：
从脑电图（EEG）进行视觉解码已成为非侵入式脑机接口（BCIs）中极具前景的途径。现有的基于EEG的解码方法大多将脑信号与深度视觉模型的最终层语义嵌入对齐。然而，依赖这些高度抽象化的嵌入不可避免地导致严重的跨模态信息不匹配。在本文中，我们引入了神经可见性的概念，并相应地提出了EEG-可见层选择策略，将EEG信号与中间视觉层对齐，以最小化这种不匹配。此外，为了适应人类视觉处理的多阶段性质，我们提出了一种新的分层互补融合（HCF）框架，该框架联合整合不同层次的视觉表示。广泛的实验表明，我们的方法在THINGS-EEG数据集上的零样本视觉解码中达到了84.6%的准确率（+21.4%）。此外，我们的方法在多种EEG基线中实现了高达129.8%的性能提升，展示了其强大的通用性。
英文摘要：
Visual decoding from electroencephalography (EEG) has emerged as a highly promising avenue for non-invasive brain-computer interfaces (BCIs). Existing EEG-based decoding methods predominantly align brain signals with the final-layer semantic embeddings of deep visual models. However, relying on these highly abstracted embeddings inevitably leads to severe cross-modal information mismatch. In this work, we introduce the concept of Neural Visibility and accordingly propose the EEG-Visible Layer Selection Strategy, aligning EEG signals with intermediate visual layers to minimize this mismatch. Furthermore, to accommodate the multi-stage nature of human visual processing, we propose a novel Hierarchically Complementary Fusion (HCF) framework that jointly integrates visual representations from different hierarchical levels. Extensive experiments demonstrate that our method achieves state-of-the-art performance, reaching an 84.6% accuracy (+21.4%) on zero-shot visual decoding on the THINGS-EEG dataset. Moreover, our method achieves up to a 129.8% performance gain across diverse EEG baselines, demonstrating its robust generalizability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
