AI 中文总结
本研究针对无标签跨主体EEG到图像检索性能差的问题，提出SCORE框架，通过源训练与部署时坐标对齐，在两个基准上显著提升检索准确率，推动脑视觉解码的实际应用。
AI 中文摘要
准确的视觉解码可揭示大脑如何表征视觉信息，并从脑电图（EEG）等神经信号中恢复感知内容，具备神经通信的潜力。然而，当前的EEG到图像检索方法在无标记校准的新用户上的表现远低于同主体方法，限制了其实际部署。为理解这一差距，我们分析了不同主体间的EEG特征，发现不同主体虽保留了概念间相似的关系，但会沿不同的坐标方向表达这些关系。因此，我们提出了Subject Coordinate Recovery（SCORE），这是一种结合恢复感知源训练与部署时坐标对齐的无目标标签框架。训练期间，SCORE将源主体的EEG与公共图像空间对齐，并通过仅源域的片段模拟未见过主体的恢复；部署时，在两个编码器均冻结的情况下，SCORE通过修正中心性的匹配选择可靠的EEG-图像地标，并估计正交变换以恢复目标主体的EEG坐标，无需源数据或目标标签。在两个公共基准的200路检索任务中，SCORE在每个目标主体上均优于未适配的基线，且达到最佳整体准确率：在THINGS-EEG2和Alljoined-1.6M上分别取得Top-1/Top-5准确率53.23%/83.55%和12.01%/32.16%，较最强基线分别提升17.45/15.70和3.08/4.62个百分点。无需目标标签或编码器更新，SCORE使基于大脑的视觉解码更接近跨用户的鲁棒、实用、低延迟部署。
英文摘要
Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potential for neural communication. However, current EEG-to-image retrieval methods perform far below their within-subject counterparts for new users without labeled calibration, limiting real-world deployment. To understand this gap, we analyze EEG features across subjects and find that different subjects preserve similar relationships among concepts but express them along different coordinate directions. We therefore propose Subject Coordinate Recovery (SCORE), a target label-free framework combining recovery-aware source training with coordinate alignment at deployment. During training, SCORE aligns source subject EEG with a common image space and simulates unseen-subject recovery through source-only episodes. At deployment, with both encoders frozen, SCORE selects reliable EEG-image landmarks through hubness-corrected matching and estimates an orthogonal transformation to recover target EEG coordinates without source data or target labels. In 200-way retrieval on two public benchmarks, SCORE outperforms the unadapted baseline for every target subject and achieves the best overall accuracy. It reaches 53.23%/83.55% and 12.01%/32.16% Top-1/Top-5 on THINGS-EEG2 and Alljoined-1.6M, respectively, surpassing the strongest baselines by 17.45/15.70 and 3.08/4.62 percentage points. Without target labels or encoder updates, SCORE brings brain-based visual decoding closer to robust, practical, low-latency deployment across users.
Comments9 pages, 6 figures
