GACE: Guidance, Assessment, and Correction for EEG Foundation Models

EEG foundation models (EFMs) learn rich internal knowledge from large-scale EEG pretraining, encoding transferable spatiotemporal representations and task-relevant priors. We observe that such knowledge is expressed not only in the final Decision Result of an EFM, but also through Decision Evidence, Decision Trajectory, and training-time Decision Dynamics. These internal observations reveal whether an EFM prediction is reliable and whether a misclassified sample still preserves latent support for the correct class. This work aims to turn the internal decision structures of EFMs into actionable signals for self-guidance, self-assessment, and self-correction. We propose GACE, a plug-and-play framework that uses EFM Decision Dynamics to guide fine-tuning, performs reliability modeling around the Decision Result and Decision Evidence, and further incorporates Decision Trajectory to correct high-risk predictions, improving decision reliability without modifying the EFM backbone.