PACE: PROGRESSIVE AFFECTIVE ALIGNMENT FOR EEG FOUNDATION MODELS

EEG foundation models can encode useful affective information, but their representations are not necessarily organized for transfer across heterogeneous subjects, datasets, and elicitation contexts. We diagnose frozen representations from four EEG foundation models and find that emotion relations are substantially more stable within subjects than across heterogeneous datasets and elicitation contexts, revealing a gap between recoverable affective information and transferable affective organization. We introduce Progressive Affective Contrastive Enhancement (PACE), an affective post-training framework that progressively expands affective alignment from reliable within-dataset relations to heterogeneous datasets and elicitation contexts. PACE first learns cross-subject affective structure within individual datasets, then extends alignment across datasets and elicitation contexts while retaining within-dataset supervision. We evaluate PACE on twelve completely held-out target datasets and four foundation backbones. PACE improves average full-fine-tuning balanced accuracy by 2.66–7.05 percentage points and linear-probing performance by 3.52–5.77 points. Moreover, PACE outperforms CE, SupCon, and VREx post-training approaches on all twelve target datasets. Stage ablations support the progressive strategy, and frozen-representation analyses show improved emotion-relation geometry across all four backbones. Together, these results establish affective post-training as an effective bridge between generic EEG pretraining and transferable affective representations across heterogeneous datasets and elicitation contexts.Code is available at https://anonymous.4open.science/r/ICLR2027_PACE/