A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition

Neuroscience shows that the brain stimulated by external information can induce functional responses to emotions, which can be measured and analyzed by electroencephalogram (EEG). Most existing works focus on extracting specific spatial topological information and temporal dependency representations, with a few works begin to mine the value of spatiotemporal cross-domain information. However, these approaches overdependence on cognitive prior information, limiting their ability to grasp complex domain-structured information. Moreover, the stable extraction of cognitive functions is crucial for reinforcing the performance of emotion recognition systems. Here, we propose a multi-domain constraint learning system. It is inspired by adaptive cognitive graphs, embedding spatiotemporal representative knowledge into the constructed framework (AC-DCL) to improve the performance of emotion recognition. In the AC-DCL, a spatial-guided dynamic graph constraint learning module is meticulously designed to overcome reliance on cognitive priors and adaptively generate and constrain functional relationships within cognitive graphs. At the same time, a temporal-driven sequence transformer is proposed to extract global temporal dependency features. Furthermore, this study designs a novel multi-domain interactive attention module with constraining domain-specific differences and aggregating complementary information, which surpasses traditional static cross-domain interactions. The essence of the proposed AC-DCL lies in capturing stable cognitive functions from complex and dynamic cognitive structures. Experimental results on the DREAMER, FACED, and SEED-IV datasets demonstrate the impressive advantages of AC-DCL and its potential to drive the learning of cross-domain interaction representations.