The paper introduces NEAR, a neural‑anchor‑based retrieval framework that improves brain‑to‑image retrieval when only a few neural trials are available. By treating the high‑repetition center as an anchor, a denoiser pulls noisy queries toward it while a small network predicts pseudo anchors for candidate images, aligning both neural and visual representations. Across EEG, MEG, and fMRI datasets, NEAR consistently boosts retrieval accuracy, notably raising 200‑way Top‑1 accuracy on THINGS‑EEG2 by 5.7–9.3 percentage points with just one to four repetitions.
SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval proposes a new framework that aligns EEG signals from different subjects into a common image space without requiring labeled calibration data. By training on source subjects and estimating an orthogonal transformation at deployment, SCORE recovers target EEG coordinates and selects reliable EEG-image landmarks through hubness-corrected matching. The method achieves state‑of‑the‑art Top‑1/Top‑5 accuracy on two public benchmarks, outperforming existing baselines by significant margins.
By Zhenyao Cui, Siyuan Kan, Siyang Li, Ziwei Wang, Dongrui Wu
arXiv:2609.24136v1 Announce Type: new
Abstract: Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented...
By Ye Wang, HaoKun Ren, Hong Yu, Ruirui Li, Xiao Li, Ke Liu, Wei Wu
The paper introduces an adaptive cortically constrained method for aligning EEG signals with visual representations in zero‑shot brain‑to‑image retrieval. It reconstructs EEG into ROI‑level source patterns, encodes them with a Neuro‑ROI Attention Encoder, and applies evidence‑based adaptive visual supervision to account for response‑wise variability. On the THINGS‑EEG dataset, the approach achieves strong 200‑way retrieval performance and offers ROI‑level attribution for interpretability.
By Ye Wang, Haokun Ren, Wei Wu, Guoyin Wang, Zhuliang Yu, Hong Yu, Ke Liu
Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented by pretrained vision models, whose internal repr...
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
By Kanglei Zhou, Chunyan Lan, Dongyang Li, Jun Zhu, Liyuan Wang
The paper presents a reproducible single‑subject baseline for reconstructing visual stimuli from EEG using a temporal‑spatial convolutional encoder that maps averaged EEG signals to 512‑dimensional ViT-B/32 image features. On the THINGS‑EEG2 dataset, the model achieves 12.83%, 39.17%, and 58.00% image recall at ranks 1, 5, and 10, respectively, outperforming analytical chance levels. The study also shows that performance drops sharply when applying a model trained on one subject to others, and that direct conditional generators without external visual weights produce noise‑dominated outputs, indicating that only coarse semantic decoding is feasible under the tested protocol.
By Harshit Goyal
arXiv:2607. 12364v1 Announce Type: cross Abstract: EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning.
By Sukriti Tiwari, BHVSP Subrahmanyam, Nidhi Goyal, Sai Amrit Patnaik
The paper introduces CERES, a closed‑loop multimodal indexing framework that addresses semantic collapse in multimodal generation by building a three‑level semantic pyramid and using scale‑routed cross‑attention to generate images that remain retrievable by their original queries. CERES employs a co‑occurrence‑aware router, a lightweight U‑Net generator, and a soft‑Jaccard coverage objective to ensure generated images cover the intended concepts, verified by re‑indexing with a frozen vision‑language model and an external DINOv2 probe. Experiments on four pansharpening benchmarks show state‑of‑the‑art performance, especially under extreme scale variation, and significant improvements in concept‑query retrieval and image‑text ranking metrics.
By Guangyuan Dong, Chuang Liu, Haoyu Wang, Yangchen Zeng, Jiaqi Zhang, Li Jiuxing, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin, Alexander Lim Han Yang, Yusen Wu
arXiv:2608. 20810v1 Announce Type: cross Abstract: Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, so the output must remain retrievable by the queries it was meant to serve.
By Guangyuan Dong, Chuang Liu, Yangchen Zeng, Haoyu Wang, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin
arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
By Yiheng Liu, Chuhang Zheng, Peiliang Gong, Jingtao Liu, Daoqiang Zhang, Qi Zhu
arXiv:2607. 18344v2 Announce Type: replace-cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
By Yiheng Liu, Chuhang Zheng, Peiliang Gong, Jingtao Liu, Daoqiang Zhang, Qi Zhu