Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 201-209.doi: 10.11896/jsjkx.240500087

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Emotion Recognition Based on Brain Network Connectivity and EEG Microstates

FANG Chunying, HE Yuankun, WU Anxin   

  1. School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
  • Received:2024-05-21 Revised:2024-10-10 Published:2025-07-17
  • About author:FANG Chunying,born in 1978,Ph.D,professor,is a member of CCF(No.T0502M).Her main research interests include machine learning,pattern recognition and brain science.
    HE Yuankun,born in 1999,postgraduate.His main research interests include machine learning,pattern recognition and brain science.
  • Supported by:
    Heilongjiang Provincial Colleges and Universities Basic Scientific Research Business Fund(2023-KYYWF-0538).

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Abstract: As neuroscience and computational methods continue to advance,researchers have become increasingly interested in the relationship between emotion and brain activity.In this field,the connectivity of complex networks and EEG microstates have become hot research topics.The connectivity of brain networks reveals the degree of information transmission and coordination between different brain areas,which has an important impact on the emotion regulation process.Microstate is the stable activity pattern of the brain in a short period of time in the resting state,and its changes reflect the transformation of the brain's functional state.In order to further study the relationship between emotions and various brain regions and improve the accuracy of emotion recognition,this paper proposes an emotion recognition method based on brain network module connectivity and brain electrical microstates.This method uses network module connectivity analysis to modularize complex systems to reveal the relationship between the whole and parts under different emotions.At the same time,microstate analysis is introduced to explore the correspondence between brain areas and emotions,and the duration,occurrence frequency,coverage ratio and transition probability of each microstate are extracted as features for emotion recognition.It is found that emotions are more active in the right hemisphere.Finally,in order to get more comprehensive feature information,the two features are spliced and fused for emotion recognition.A lot of experiments are conducted on the SEED dataset,and the experimental results show that the highest average accuracy is obtained for the module connectivity feature gamma band with 94.07% accuracy,the microstate feature with 87.23% accuracy,and the fusion feature with 95.34% accuracy,which is increased by 1.27% and 8.11% respectively compared with the accuracy of feature extraction and recognition of the above single method.

Key words: Emotion recognition, Brain network, Module connectivity, Microstate, Feature fusion

CLC Number: 

  • TP391
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