Emotion recognition using electroencephalography (EEG) signals has attracted increasing attention in recent years. However, existing methods often lack generalization in cross-corpus settings, where a model trained on one dataset is directly applied to another without retraining, due to differences in data distribution and recording conditions. To tackle the challenge of cross-corpus EEG-based emotion recognition, we propose a novel framework termed Soft Contrastive Masked Modeling (SCMM). Grounded in the theory of emotional continuity, SCMM integrates soft contrastive learning with a hybrid masking strategy to effectively capture emotion dynamics (refer to short-term continuity). Specifically, in the self-supervised learning stage, we propose a soft weighting mechanism that assigns similarity scores to sample pairs, enabling fine-grained modeling of emotional transitions and capturing the temporal continuity of human emotions. To further enhance representation learning, we design a similarity-aware aggregator that fuses complementary information from semantically related samples based on pairwise similarities, thereby improving feature expressiveness and reconstruction quality. This dual design contributes to a more discriminative and transferable representation, which is crucial for robust cross-corpus generalization. Extensive experiments on the SEED, SEED-IV, and DEAP datasets show that SCMM achieves state-of-the-art (SOTA) performance, outperforming the second-best method by an average accuracy of 4.26% under both same-class and different-class cross-corpus settings. The source code is available at https://github.com/Kyler-RL/SCMM.