Perceptual-Prior Alignment with Subject-Adaptive Temperature for EEG-to-Image Retrieval

EEG-to-image retrieval aims to identify perceived visual stimuli from non-invasive neural recordings, but remains challenging because EEG is noisy, spatially coarse, and highly variable across trials and subjects. Existing contrastive approaches commonly align EEG with a single, fixed visual representation using one temperature shared across all subjects. This formulation overlooks both trial-level variation in the visual information represented by EEG and subject-level heterogeneity in neural separability. We propose Perceptual-Prior Alignment with Subject-Adaptive Temperature (PPA-SAT), a retrieval-oriented framework that calibrates cross-modal alignment at two complementary resolutions. At the trial level, Perceptual-Prior Alignment (PPA) retains a heterogeneous bank of raw, blurred, noisy, low-resolution, and mosaic visual candidates. It constructs an EEG-conditioned target through residual attention fusion. At the source-subject level, Subject-Adaptive Temperature (SAT) complements this alignment by learning lightweight subject-specific offsets to a shared contrastive temperature, enabling subject-aware calibration with negligible parameter overhead. Experiments on THINGS-EEG 200-way zero-shot retrieval demonstrate consistent improvements under both intra- and inter-subject protocols. PPA-SAT achieves 91.7% Top-1 and 99.0% Top-5 intra-subject accuracy, exceeding the strongest prior method by 9.1 and 1.3 percentage points, respectively. Under leave-one-subject-out evaluation, it achieves 45.8% Top-1 and 72.7% Top-5 accuracy, outperforming the strongest baseline by 21.8 and 17.1 points. These results demonstrate the complementary value of trial-adaptive visual alignment and subject-aware contrastive calibration for robust EEG-to-image retrieval.