Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks
Objective ablations and representation analyses show that invariance-oriented pre-training improves transfer and organizes subject-sensitive neural representations beyond reconstruction alone, establishing invariance learning as a promising principle for building reusable EEG foundation models.