SIFR: Does Session-Invariant Factorized Representation Improve Cross-Session EEG Identity Recognition?

Cross-session EEG identity recognition faces a direct tension: a model has to keep the stable cues that distinguish one person from another, while still adapting to changes caused by headset re-wearing, acquisition conditions, and task state. We propose SIFR, a Session-Invariant Factorized Representation framework that turns this problem into controlled residual compensation inside the normalization layer. Its core module, FactorNorm, adds an input-conditioned session branch on top of shared affine parameters. A low-rank gate adjusts the compensation dimension by dimension, while zero-residual initialization keeps the starting point exactly aligned with LayerNorm. Session classification supervision gives the compensation branch an information target, and the Orthogonal Prototype Constraint (OPC) limits directional overlap with the current identity prototype. Under explicit assumptions, we derive a linear response bound for this geometric constraint and show how it limits perturbations along a specified direction. Across session-wise records on PhysioNet, MCD, and SEED-IV, SIFR reaches mean identification accuracies of 99.28%, 82.14%, and 72.34%, improving over the strongest external mean-ACC baseline on each dataset by 0.21, 4.85, and 3.06 percentage points, respectively. Ablation and identity-parameter freezing experiments further examine the roles of gating, geometric regularization, and restricted parameter updates. The results show that organizing cross-session representation learning around explicit compensation can improve identification accuracy while also making genuine and impostor identity claims more separable in score space; the component analysis further exposes the roles played by the gate and the geometric constraint.