Can Cross-Identity Session-Shift Transfer Improve EEG Identification Across Sessions?

Cross-session EEG identification requires identity-discriminative representations to remain stable despite changes in cognitive state and acquisition conditions. We propose SHIFT-ID, a cross-identity session-shift transfer framework for the setting in which registered users provide no calibration data in their later evaluation sessions.SHIFT-ID constructs an empirical drift bank from paired sessions of auxiliary identities in the space of feature means and log standard deviations. It then transfers drifts across identities to generate session-informed training views and combines cosine consistency between representations with worst-view prototype regularization to preserve identity structure under these perturbations. At inference time, the fixed encoder, projection head, and classifier process each query independently, without calibration recordings from evaluation users or online parameter updates. We establish a unified information-isolation protocol on MCD, M3CV, and SEED-IV, evaluate twelve commonly used or recent EEG architectures, and perform information-budget-matched comparisons, transfer-direction analyses, mechanism ablations, and perturbation diagnostics. All experiments are repeated five times, and performance is reported as the mean over the five runs. The full configuration achieves mean Rank-1 accuracies of 70.94%, 77.83%, and 85.06% on the three datasets, respectively. Relative to CAGCNet, the strongest source-only external baseline under our protocol, the corresponding gains are 0.55, 0.42, and 0.38 percentage points. Under the same backbone and auxiliary-target budget, the Rank-1 gains over DANN are 0.63, 0.31, and 0.27 percentage points. Across the evaluated conditions, the directional, ablation, and perturbation analyses consistently support the contribution of empirical drift transfer and robust prototype regularization, while the modest absolute margins motivate a conservative interpretation of the improvements.