Representation, Normalization, and Sequential Calibration for Cross-Subject EEG-to-Image Retrieval

EEG-to-image retrieval is commonly formulated as a cross-modal representation-learning problem, but cross-subject deployment introduces an additional train-test mismatch: subject-specific normalization statistics available during source training are unavailable for an unseen subject at cold start. We present the Representation-Normalization-Sequential Calibration (RNSC) framework, which treats shared representation learning, source-subject normalization, and unseen-subject calibration as coordinated stages of a single retrieval space. RNSC couples a shared, subject-agnostic visual reference with an EEG encoder that factorizes temporal structure before channel-semantic mixing and aggregates repeated trials after latent encoding. During source training, EEG latents are optimized in subject-standardized coordinates; at deployment, target moments are estimated sequentially from a finite source prior and past unlabeled target EEG under a strict predict-before-update protocol. On THINGS-EEG2, the shared representation achieves state-of-the-art intra-subject 200-way retrieval, with 93.67% Top-1 and 99.36% Top-5, while the complete RNSC framework reaches 48.10% Top-1 and 76.34% Top-5 under inter-subject evaluation. Controlled ablations, latent-geometry diagnostics, and cold-start analyses disentangle the complementary roles of representation learning, training-time normalization, and sequential target-statistic estimation.