EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits
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 evalua...
| Authors | Kaifan Zhang, Lihuo He, Yuqi Ji et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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