Subject-specific distribution shifts represent an important obstacle to the development of foundation models for EEG decoding. To address this, we propose {\method}, an adaptive layer designed as a drop-in replacement for standard linear or convolutional layers in any neural network architecture. Our layer captures subject-specific variability by decomposing its weights into a shared, subject-invariant component and a lightweight, low-rank correction unique to each subject. This explicit separation of general knowledge from personalized adaptation allows existing models to become robust to subject shifts. Empirically, models equipped with our layer %substantially outperform both a shared-weight-only model (subject-agnostic model) and the average of individually trained subject-specific models. Consequently, the {\method} offers a practical and scalable path towards building effective cross-subject foundation models for EEG.