Abstract Cross-subject emotion recognition based on multimodal physiological signals has broad application prospects in fields such as human-computer interaction and health monitoring. However, the dual challenges of multimodal heterogeneity and cross-subject differences severely restrict the generalization ability of the model. The existing methods mainly focus on static decoupling and the study of emotional correlation, but lack attention to dynamic interaction and emotional causal relationships. To address these issues, this paper proposes a Meta-causal Bidirectional Decoupled Learning (MBDL) framework. This framework consists of a bidirectional interaction disentanglement (BID) module and a meta-causal learning (MCL) module. Specifically, the BID module decomposes the representation into three subspaces of emotion, identity and modality, and allows structured mutual modulation between them, which simulates the complex interaction of multiple factors in the process of emotion generation; Secondly, the MCL module not only enables the model to adapt quickly when the new agent only provides a small number of samples by meta learning, but also the causal inference mechanism imposes stability constraints through counterfactual generation, forcing the model to learn stable causal relationships rather than spurious correlations. Extensive experiments on the DEAP and MAHNOB-HCI public datasets have shown that the MBDL framework significantly outperforms the existing state-of-the-art methods under the cross-validation protocol, and has strong generalization performance in cross-subject experiments.