Rest-Tuning: Data-Efficient Adaptation of EEG Foundation Models to Individuals via Resting-State Signals

EEG foundation models (FMs) offer strong population-level priors but degrade on individual subjects due to anatomy-driven distribution shifts. We propose Rest-Tuning, a task-agnostic calibration framework that personalizes a population FM using just 1–3 minutes of unlabeled resting-state EEG. Rest-Tuning employs masked teacher-student alignment to produce a subject-calibrated backbone, reused as initialization across multiple tasks from the same subject. Across two multi-subject datasets and four EEG FMs, Rest-Tuning outperforms task-only adaptation, with up to 13.3% absolute gain in downstream task accuracy and over 40% average reduction in labeled task data. Calibration saturates quickly and requires under 3 minutes of compute per subject. Representation analyses show reduced subject-to-population distance and smoother LoRA adapter interpolation paths, indicating improved compatibility between rest- and task-adapted solutions. These results establish resting-state EEG as a practical, fast, and reusable calibration interface, enabling population EEG FMs to function as reliable, subject-specific BCIs and serve as reusable initialization for downstream task adaptation.