UniPER: Towards a Unified Predictive Model of Affective EEG

EEG emotion recognition requires transferable representations, but general EEG pretraining often draws on clinical and other non-affective recordings. Learning transferable emotion representations from multiple affective datasets remains challenging because stimuli, participants, sensor layouts, and annotation schemes differ across sources. We introduce UniPER, a latent predictive framework that learns shared affective EEG representations without requiring source emotion labels or cross-dataset label alignment. UniPER trains a montage-aware online encoder to predict masked latent content from a full-view target encoder updated by an exponential moving average (EMA). Position-matched content subtracts each encoder's zero-waveform response at the same sensor and time location; a matched zero reference also centers the predictor output. Masked prediction and context identity learn from waveform content and source-local recording events, while within and cross-source relations organize latent similarities. We pretrain UniPER on nine affective EEG datasets and evaluate it on six held-out target datasets under subject-dependent, subject-independent, and zero-shot protocols, retaining each target's emotion labels. Under subject-dependent adaptation, UniPER achieves the highest balanced accuracy on five of six targets, reaching 65.60% on FACED, 5.97 percentage points above the strongest evaluated baseline. Component ablations examine zero references, local prediction, context identity, source relations, and representation regularization; their effects vary by target and metric. These results support multi-source affective pretraining through latent prediction as a route to reusable emotion representations across heterogeneous EEG datasets.