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

arXiv 2607.27857 ↗First posted 30 Jul 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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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References · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  3. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,419
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,163
  5. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  6. 2023MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editingcited by 615
  7. 2022When and why vision-language models behave like bags-of-words, and what to do about it?cited by 603
  8. 2022Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionalitycited by 523
  9. 2023SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionalitycited by 238
  10. 2024AnyEdit: Mastering Unified High-Quality Image Editing for Any Ideacited by 235
  11. 2022Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpaintingcited by 229
  12. 2024HQ-Edit: A High-Quality Dataset for Instruction-based Image Editingcited by 191
18 more
  1. 2022CREPE: Can Vision-Language Foundation Models Reason Compositionally?cited by 177
  2. 2021VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomenacited by 134
  3. 2022VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relationscited by 113
  4. 2017FOIL it! Find One mismatch between Image and Language captioncited by 95
  5. 2023Decoding Natural Images from EEG for Object Recognitioncited by 79
  6. 2024Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusioncited by 79
  7. 2022Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Featurescited by 63
  8. 2024I2EBench: A Comprehensive Benchmark for Instruction-based Image Editingcited by 61
  9. 2025Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Priorcited by 28
  10. 2023Learning Robust Deep Visual Representations from EEG Brain Recordingscited by 26
  11. 2024CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Informationcited by 26
  12. 2025NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignmentcited by 13
  13. 2025NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagerycited by 7
  14. 2025Exploring The Visual Feature Space for Multimodal Neural Decodingcited by 4
  15. 2025Shrinking the Teacher: An Adaptive Teaching Paradigm for Asymmetric EEG-Vision Alignmentcited by 4
  16. 2026HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignmentcited by 4
  17. 2025Multigranular Evaluation for Brain Visual Decodingcited by 2
  18. 2026CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignmentcited by 2

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