提出无标签测试时校准方法，提升跨被试脑电图像检索精度与稳定性。
SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval
- 基于相似矩阵设计几何与结构双专家，实现无标签校准。
- 在THINGS-EEG2上提升Top-1/Top-5准确率，降低簇聚现象与类别不平衡。
- 适用于多种脑电编码器，可作为通用测试时校准模块。
跨被试脑电到图像检索面临被试差异和嵌入空间中的簇聚问题，导致相似性几何失真与top-k排序不稳定，使小k短列表不可靠。本文提出SATTC（结构感知无标签测试时校准），一个直接作用于冻结脑电与图像编码器相似矩阵的无标签校准头。SATTC结合几何专家：自适应脑电嵌入去相关与改进的跨域相似性局部缩放（CSLS）；以及结构专家：基于互近邻、双向top-k排名和类别流行度，通过简单的乘积专家规则融合。在严格留一被试出协议下的THINGS-EEG2数据集上，采用余弦相似度、L2归一化嵌入和候选去相关已构建强基线，优于原始ATM检索设置。在此基础上，SATTC进一步提升Top-1与Top-5准确率，减少簇聚与类间不平衡，生成更可靠的短列表。该效果在多个脑电编码器上均有效，表明SATTC可作为编码器无关、无标签的测试时校准层，适用于跨被试神经解码任务。
原文摘要 · Abstract (English)
Cross-subject EEG-to-image retrieval for visual decoding is challenged by subject shift and hubness in the embedding space, which distort similarity geometry and destabilize top-k rankings, making small-k shortlists unreliable. We introduce SATTC (Structure-Aware Test-Time Calibration), a label-free calibration head that operates directly on the similarity matrix of frozen EEG and image encoders. SATTC combines a geometric expert, subject-adaptive whitening of EEG embeddings with an adaptive variant of Cross-domain Similarity Local Scaling (CSLS), and a structural expert built from mutual nearest neighbors, bidirectional top-k ranks, and class popularity, fused via a simple Product-of-Experts rule. On THINGS-EEG2 under a strict leave-one-subject-out protocol, standardized inference with cosine similarities, L2-normalized embeddings, and candidate whitening already yields a strong cross-subject baseline over the original ATM retrieval setup. Building on this baseline, SATTC further improves Top-1 and Top-5 accuracy, reduces hubness and per-class imbalance, and produces more reliable small-k shortlists. These gains transfer across multiple EEG encoders, supporting SATTC as an encoder-agnostic, label-free test-time calibration layer for cross-subject neural decoding.
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