The Impact Of Transient And Stable Patterns Of Functional Connectivity In Emotion Recognition

Abstract

The achievement of emotional functions relies on the interactions of various functional systems of the human brain. Numerous studies tried to explore the mechanism of emotions based on functional connectivity. However, the contribution of the transient and stable patterns of brain communication in brain emotions was still unclear. The activity of functional connectivity (AFC) and background of functional connectivity (BFC), according to the recently developed activation network paradigm, indicate transient and stable patterns of functional connection, respectively. In this work, we evaluated the performance of the transient and stable patterns in emotional activities by using the activation network architecture to the SEED-IV dataset in order to achieve emotion recognition. The top 100 critical connections of respectively AFC and BFC of each subject were extracted by a data-driven feature selection strategy. The critical connections across all subjects of both AFC and BFC suggested the importance of the Gamma band in emotion recognition. Especially, AFC and BFC showed different communication modes during the emotions. Finally, the subject-independent classification was employed on each subject’s critical connections to achieve emotion recognition. The BFC showed the best classification accuracy of 78.71% ± 1.73% (mean ± std). The findings demonstrated that human emotions were mostly influenced by the stable brain communication patterns. The findings of this investigation offer a new insight into the studies of human emotion.