Cross-subject emotion recognition based on electroencephalogram (EEG) signals faces significant challenges, mainly because EEG data are highly non-stationary and easily influenced by time, environment, and individual physiological states. Meanwhile, substantial inter-subject variability leads to obvious differences in signal patterns across different people, which makes it difficult for a single model to learn stable and transferable emotional features. As a result, these factors severely hinder model generalization and reduce recognition performance in real-world applications. Unlike previous reviews that categorize methods based on network architectures, this paper proposes a novel taxonomy grounded in the “generalization hypothesis,” synthesizing existing approaches into five major paradigms: statistical and adversarial distribution alignment, topological and structural modeling, advanced representation learning, generative modeling and style reconstruction, and multimodal complementary fusion. Our analysis reveals that the core conflict lies in the trade-off between alignment intensity and semantic integrity. Future research should integrate causal representation learning with source-free domain adaptation to realize truly plug-and-play affective brain–computer interfaces (aBCIs).