Abstract
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-EEG 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.
Abstract (translated)
跨被试脑电图-图像检索的视觉解码面临嵌入空间中的被试偏移与枢纽效应挑战，这些因素会扭曲相似性几何结构并 destabilize 前k名排序，导致小k候选列表不可靠。我们提出SATTC（结构感知测试时校准），这是一个无需标签的校准模块，可直接作用于冻结的脑电图与图像编码器的相似度矩阵。SATTC融合几何专家（对脑电图嵌入进行被试自适应白化处理，并结合跨域相似度局部缩放的 adaptive 变体）与结构专家（基于互最近邻、双向前k排序及类别流行度构建），通过简单的专家乘积规则进行融合。在THINGS-EEG数据集上采用严格的留一被试交叉验证协议时，使用余弦相似度标准化推理、L2归一化嵌入及候选白化处理，已能在原始ATM检索框架上建立强跨被试基线。在此基线基础上，SATTC进一步提升了Top-1与Top-5准确率，降低了枢纽效应与类别不平衡问题，并产生更可靠的小k候选列表。这些增益可迁移至多种脑电图编码器，支持SATTC作为跨被试神经解码中与编码器无关的免标签测试时校准层。
URL
https://arxiv.org/abs/2603.20738
