Functional Integration and Separation of Brain Network Based on Phase Locking Value During Emotion Processing

The functional connection patterns of the brain during the processing of human emotions are complex and changeable. Therefore, the use of brain network information at the global scale is not sufficient to reflect the coupling relationship between brain regions, and it is impossible to accurately analyze the differences in information interaction patterns of the brain under different emotions. The purpose of this article is to study the functional connectivity of the brain by using the phase synchronization of electroencephalogram (EEG) channel information in different emotional states. Considering that the phase locking value (PLV) can effectively reflect the phase synchronization relationship between the EEG channels, it is adopted to describe the functional connection relationship of the brain. After the brain network based on PLV is constructed, considering that the topological structure of the brain network is complex and volatile, we merge the two types of attributes: 1) functional integration and 2) functional separation, to analyze the differences in brain connectivity for different emotions. Furthermore, the modular structure of the brain network is constructed through community detection to extract its more comprehensive local characteristics for connectivity analysis. The results show that compared with positive emotions, brain regions under negative emotions have higher phase synchronization, more complex brain connectivity patterns, and more obvious modular structures. In addition, key cortical brain regions associated with emotional stimulation have been identified as brain network hubs. The verification on the DEAP data set demonstrates that the analysis framework of this study effectively improved the accuracy of emotion recognition.