Beyond Trial Averaging: Anchoring Neural and Visual Representations for Few-Repetition Brain-to-Image Retrieval
Score
78揭示了非传递对齐这一新机制，挑战了仅将性能下降归因于查询噪声的传统认知。
方法设计合理，多个数据集验证充分，但缺少与更多基线方法的全面对比。
显著降低神经数据重复采集需求，直接提升脑-机接口和神经检索的现实可用性。
当前脑机接口和少样本神经解码属于前沿热点，该论文贴近最新研究需求。
来自arXiv预印本平台，尚未经同行评审，但作者团队背景和数据集公开性强。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper offers problem diagnosis, methodological innovation, and cross-modal validation, representing a significant contribution to few-shot neural retrieval.
AI commentary
「What makes this paper truly valuable is that it re-examines the 'few-repetition retrieval collapse' phenomenon, usually attributed to noise, and uncovers a subtler representation alignment structure. For researchers in brain-computer interfaces and neural decoding, NEAR could mean future experiments no longer need subjects to view images dozens of times—images could be retrieved from a single trial. Though it's a preprint without formal peer review, the method's validation across three neuroimaging modalities is convincing.」
Quick overview
Brain-to-image retrieval typically requires averaging up to 80 neural trials per image to suppress noise; when only one or a few repetitions are available, retrieval accuracy drops sharply. This paper discovers a non-transitive alignment pattern: low-repetition query signals and image representations each align with the high-repetition center, but not directly with each other, indicating that query noise is only part of the problem and gallery placement also matters. The authors propose NEAR, a neural-anchor-based retrieval 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 improves retrieval in the few-repetition regime. On THINGS-EEG2, it improves 200-way Top-1 accuracy by 5.7 and 9.3 percentage points when averaging one and four repetitions, respectively. By anchoring neural and visual representations, NEAR reduces reliance on repeated acquisition and brings neural retrieval closer to real-world deployment.
- Identifies a non-transitive alignment pattern: low-repetition queries and image representations each align with the high-repetition center but not directly with each other, suggesting gallery placement also affects retrieval.
- Proposes NEAR, which anchors on the high-repetition center and approaches it from both sides: denoising the query and predicting pseudo-anchors from images.
- Consistently improves retrieval in the few-repetition regime across four datasets spanning EEG, MEG, and fMRI.
- On THINGS-EEG2, improves 200-way Top-1 accuracy by 5.7 and 9.3 percentage points with one and four averaged repetitions, respectively.
Full text
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