Toward Sustainable Cross-Time Affective Brain–Computer Interfaces: Explicit Modeling and Structural Calibration of Temporal Variability

Abstract Temporal variability limits the practical application of cross-time affective brain-computer interfaces (aBCI). Existing static feature-to-emotion mapping methods are unable to capture the dynamic changes in brain state. Here we propose a multi-level dynamic integrated perception network algorithm (MDIN), which explicitly models temporal variability through a three-layer framework: a temporal-spatial-spectral joint perception module, an adaptive dynamic perception matrix for feature capture, and a discriminative alignment strategy for cross-domain consistency preservation. This structure not only mitigates the impact of temporal variability on emotion recognition but also suppresses noise accumulation in weakly supervised learning scenarios. Experiments on the ECPL and SEED datasets demonstrate that MDIN achieves the highest cross-time recognition accuracies of 96.97% and 94.21%, respectively, outperforming state-of-the-art methods. Notably, as a novel structural solution for temporal variability modeling in affective neuroengineering, it provides a theoretical and technical basis for developing adaptive, long-term stable aBCI systems for mental health monitoring and human-machine interaction.