SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval
Score
78揭示了跨受试者EEG坐标偏移现象，并提出不需要标签即可恢复坐标的通用方案，新颖性强。
方法设计合理，实验覆盖两个大规模基准，结果详实且有优于强基线的显著提升。
无需目标标签和编码器更新，显著降低跨用户部署成本，对脑机接口落地具有直接价值。
属于当前多模态对齐与无监督适配研究的热点方向，且发表于近期预印本。
来源于arXiv AI/CL/LG，同行评议前但工作完整规范，基准和数据均为公开资源。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper is well-motivated with a concise method and significant gains on two large benchmarks, offering strong insights for BCI and visual decoding.
AI commentary
「This work neatly dissects the problem, focusing on cross-subject coordinate shifts rather than stacking complex networks. The label-free coordinate alignment approach yields substantial gains, making it a valuable reference for BCI and cross-user retrieval researchers, and a step toward plug-and-play neural retrieval for product teams.」
Quick overview
EEG-to-image retrieval holds great promise for neural communication, but performance drops sharply for new users without labeled calibration. This paper analyzes EEG features and finds that different subjects preserve similar concept relationships but express them along different coordinate directions, leading to the Subject Coordinate Recovery (SCORE) framework. During training, SCORE aligns source-subject EEG with a common image space and simulates unseen-subject recovery via source-only episodes. At deployment, with frozen encoders, it selects reliable EEG-image landmarks through hubness-corrected matching and estimates an orthogonal transformation to recover target coordinates without source data or target labels. On THINGS-EEG2 and Alljoined-1.6M, SCORE outperforms the strongest baselines by significant margins in 200-way retrieval, bringing practical cross-user visual decoding closer to deployment.
- Identifies that EEG features across subjects share concept structure but differ in coordinate directions, explaining the cross-subject performance gap.
- Proposes SCORE, which performs zero-shot cross-subject retrieval via coordinate alignment without target labels or encoder updates.
- Achieves 53.23% and 12.01% Top-1 accuracy on THINGS-EEG2 and Alljoined-1.6M, surpassing strongest baselines by 17.45 and 3.08 percentage points.
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