Kernelized Task-Sufficiency Regularization for EEG Foundation Model Adaptation

Adapting EEG foundation models to new tasks and individuals is essential, especially when task labels are scarce. Yet strong predictive performance does not reveal whether adaptation captures the task-relevant information available from pretraining. A residual-information audit on held-out subjects shows that frozen pretrained features can improve adapted logits in several settings, including after full fine-tuning. Motivated by this finding, we propose ReSuff, which encourages adapted representations to capture label information available in a fixed, compact view of pretrained features. Using label residuals defined by conditional class probabilities, we derive a joint Gaussian-kernel criterion that vanishes exactly at task sufficiency relative to this view at the population level. During training, ReSuff fits a kernel predictor on support subjects and evaluates residual moments on disjoint query subjects, while class-conditioned subject-adversarial learning addresses intersubject variation. Across three limited-label, subject-independent EEG benchmarks and three pretrained backbones, ReSuff improves balanced accuracy over the matched task-focused adaptation baseline in all nine backbone and dataset combinations, with an average gain of 2.3 percentage points. On REVE, it further improves the two-task mean balanced accuracy of four adaptation strategies by 1.5–2.2 points. These results show ReSuff is a practical plug-in to improve EEG foundation model adaptation without increasing inference cost.