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.
BrainBind is a framework that aligns synchronized EEG and fMRI recordings taken during long‑form naturalistic video viewing by mapping both modalities into frozen video embedding spaces, treating these spaces as common visual‑semantic references rather than literal neural codes. Experiments on the CineBrain dataset show that different system configurations yield distinct retrieval profiles, with Omni performing best on full candidate organization (MRR = 23.82, nDCG@10 = 25.31) and Joint‑Dual achieving the highest mean label‑aware R@1 (13.52%). The study highlights how repeated stimulus identity and narrative context influence retrieval performance across episode‑disjoint evaluations.
By Shixiao Wang, Rining Wu, Jian K. Liu
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
NeuroBridge is a self‑supervised framework for EEG‑to‑image decoding that uses Cognitive Prior Augmentation (CPA) to introduce perceptual variability and a Shared Semantic Projector (SSP) to align EEG and image features in a shared semantic space. The method achieves significant gains over prior work, improving top‑1 accuracy by 12.3% and top‑5 accuracy by 10.2% on a 200‑way zero‑shot retrieval task in intra‑subject experiments. Extensive tests show the approach is robust, scalable, and effective for neural visual decoding.
By Wenjiang Zhang, Sifeng Wang, Yuwei Su, Xinyu Li, Chen Zhang, Suyu Zhong
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
ProtoEEG introduces a prototype‑mediated framework for aligning EEG signals with visual data, using a shared 64‑prototype codebook to generate structured representations for retrieval and image generation. The method employs prototype contrast, distribution alignment, instance anchors, and diversity regularization to train the system, achieving state‑of‑the‑art performance on THINGS‑EEG2 and EEG‑ImageNet benchmarks. Ablation studies confirm that prototype contrast and identity preservation are critical for high accuracy, while prototype analyses reveal distributed assignments across the codebook.
By Kieren Yu, Chang Huang, Ziyang Liu, Kaishun Wu