AI 中文总结
针对少重复脑-图像检索精度骤降问题，本文提出NEAR框架，通过锚定神经与视觉表征实现性能提升，减少对重复采集的依赖，逼近实际部署要求。
AI 中文摘要
从脑信号中解码视觉信息可探究神经表征，为神经康复与梦境解码提供支持。近期脑-图像检索方法取得了可观性能，通常通过对每张图像的大量（最多80个）神经试次取平均实现，这需要重复呈现刺激，会增加延迟、成本及用户负担。当仅能获取1个或少量重复试次时，检索精度会大幅下降。该下降通常被归因于查询噪声，因为平均操作可抑制噪声并提升信号稳定性。然而，本文发现了一种非传递对齐模式：低重复试次的查询信号与图像表征均与高重复中心对齐，但二者之间并不直接对齐。该模式表明查询噪声仅为问题的一部分，图库的布置也会影响检索效果。因此，本文提出了基于神经锚的检索（NEAR）框架，将高重复中心视为锚点，从两侧逼近该锚点：去噪器将含噪查询拉向真实锚点，小型网络则从图像中预测每个候选的伪锚点并将图像拉向该伪锚点。在涵盖EEG、MEG和fMRI的四个数据集上，NEAR在少重复 regime 中均持续提升检索性能。在THINGS-EEG2数据集上，当平均1个和4个重复试次时，其分别将200路Top-1精度提升了5.7和9.3个百分点。通过锚定神经与视觉表征，NEAR减少了对重复采集的依赖，使神经检索更接近实际部署。
英文摘要
Decoding visual information from brain signals probes neural representations and enables neuro-rehabilitation and dream decoding. Recent brain-to-image retrieval approaches have achieved promising performance, typically by averaging many (up to 80) neural trials per image, requiring repeated stimulus presentation that increases latency, cost, and user burden. When only one or a few repetitions are available, the retrieval accuracy drops sharply. This drop is commonly attributed to query noise because averaging suppresses noise and increases signal stability. However, we find a non-transitive alignment pattern: the low-repetition query signal and the image representation each align with the high-repetition center, but not directly with each other. This pattern shows that query noise is only part of the problem and that gallery placement also affects retrieval. We therefore propose a neural-anchor-based retrieval (NEAR) framework that treats the high-repetition center as an anchor and approaches it from both sides: a denoiser pulls the noisy query toward the true anchor, and a small network predicts each candidate's pseudo anchor from its image and pulls the image toward it. Across four datasets spanning EEG, MEG and fMRI, NEAR consistently improved retrieval in the few-repetition regime. On THINGS-EEG2, it improved 200-way Top-1 accuracy by 5.7 and 9.3 percentage points respectively, when averaging one and four repetitions. By anchoring neural and visual representations, NEAR reduces reliance on repeated acquisition and brings neural retrieval closer to real-world deployment.
Comments8 pages, 6 figures
