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arXiv · 2607.27857

EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits

Kaifan Zhang·Lihuo He·Yuqi Ji·Junjie Ke·Lukun Wu·Tianhao You·Xinbo Gao
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Abstract

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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Kaifan Zhang, Lihuo He, Yuqi Ji, Junjie Ke, Lukun Wu, Tianhao You, Xinbo Gao. 2026-07-30. EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits. https://arxiv.org/abs/2607.27857

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