SEAM: Support-Encoded Adaptation with Meta-Learned Prompting for Few-shot Cross-Subject EEG Foundation Model Adaptation

Recent advances in deep learning have improved EEG decoding by learning task-relevant representations from neural recordings. Large-scale pretraining has further enabled EEG foundation models that capture reusable structure across subjects and tasks. However, adapting these models to an unseen subject from only a few labeled trials remains unreliable. Fine-tuning on small, subject-biased support sets can distort pretrained representations and induce negative transfer. We propose SEAM (Support-Encoded Adaptation with Meta-Learned Prompting), a target-time optimization-free framework for few-shot cross-subject EEG foundation model adaptation. SEAM jointly meta-trains the Transformer backbone, classifier head, and a lightweight prompt generator on source subjects, then freezes their weights for target adaptation. From a target support set, it constructs a global subject prototype and class residual prototypes; each query routes over these residuals to generate a personalized prompt. This preserves task-generic knowledge while expressing subject- and query-specific variations without target-time updates. Experiments on different EEG benchmarks show that SEAM better exploits limited support data, mitigates negative transfer from 2.4% to -4.8% and update-induced forgetting, and achieves a favorable accuracy–efficiency trade-off.