Work

Toward sustainable cross-time affective brain–computer interfaces: explicit modeling and structural calibration of temporal variability

Year: 2026

Type: article

Abstract: ABSTRACT Temporal variability limits the practical application of cross-time affective brain–computer interfaces (aBCIs). 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 (MDIN) algorithm, 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.

Source: National Science Review

DOI: 10.1093/nsr/nwag544

Authors: Feifan Yan, Bo Zhang, Tao Wang, Minghao Du, Shuang Liu, Dong Ming

Institution: Tianjin University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,000

Funders: National Natural Science Foundation of China, Science Fund for Distinguished Young Scholars of Tianjin