通过可控图像编辑，揭示脑电模型到底懂什么视觉信息。
EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits
- 构建可控图像编辑数据集，测试模型对对象、属性等变化的响应
- 8个模型在细粒度属性修改下性能显著下降，暴露其局限性
- 适合研究脑电视觉解码模型机制或评估其鲁棒性的学者
近期脑电到图像检索模型在从语义多样的候选集中识别观看图像方面表现优异。然而，这种成功并未揭示支持匹配的视觉信息究竟是什么。模型可能轻松从工具、植物和车辆中识别出猎豹，但能否区分原图中的猎豹与被狗替换后的同一场景？为此，我们提出EEG-EditBench，一个诊断基准，通过控制对象身份、属性、背景及对象存在性进行编辑来检验该问题。该基准基于200 THINGS-EEG2测试图像，包含2,137个质量可控的编辑，用于评估8个代表性脑电视觉解码模型。结果表明，标准检索性能强并不意味着在编辑后仍能保持一致表现，尤其是细粒度属性变化带来最大挑战。EEG-EditBench揭示了传统平均检索准确率所掩盖的模型行为，为研究脑电-图像模型保留何种视觉信息提供了受控基础。代码与完整数据集已公开。
原文摘要 · Abstract (English)
Recent EEG-to-image retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such success does not reveal what visual information supports the match. A model may readily identify a cheetah among tools, plants, and vehicles, but can it still distinguish the viewed cheetah from the same scene with the cheetah replaced by a dog? Motivated by this question, we introduce EEG-EditBench, a diagnostic benchmark that examines this question through controlled edits of object identity, attributes, background, and object presence. Built from the 200 THINGS-EEG2 test images, EEG-EditBench contains 2,137 quality-controlled edits and evaluates eight representative EEG visual decoding models. Our results show that strong standard retrieval does not consistently transfer to edit-based evaluation, with fine-grained attribute changes presenting the greatest challenge. EEG-EditBench reveals model behavior hidden by aggregate retrieval accuracy and provides a controlled basis for studying what visual information EEG-image models preserve. The code and complete dataset are publicly available.
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