PairCal: Training-Free Paired-Support Calibration for Cross-Subject EEG-to-Image Retrieval

Cross-subject EEG-to-image retrieval remains highly challenging due to substantial inter-subject variability. Existing methods primarily address this issue by learning subject-agnostic representations for cross-modal alignment. In contrast, few studies have explored how to adapt pretrained retrieval models to unseen subjects using limited examples. We propose PairCal, a training-free calibration framework for adapting retrieval to unseen subjects with only a small set of paired EEG–image support examples. PairCal calibrates both EEG queries and image candidates through two complementary components. Support-Guided Calibration (SGC) uses EEG–image residuals from the support set to derive query-adaptive cross-modal corrections. Meta-Distractor Calibration (MDC) constructs pseudo-unseen queries from the same support set via a leave-one-out procedure to estimate and suppress candidate-specific hubness bias. Together, these components enable effective few-shot adaptation to unseen subjects without updating the pretrained encoders. Extensive experiments across held-out subjects, encoder architectures, training schemes, and support-set sizes demonstrate consistent retrieval gains.