Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (1): 49-58.doi: 10.11959/j.issn.2096-6652.202105

• Special topic:emotional brain computer interface • Previous Articles     Next Articles

Emotional state decoding using EEG-based microstates of functional connectivity

Xinke SHEN1,2, Yichao LI1,2, Jin LIU1,2, Sen SONG1,2, Dan ZHANG2,3   

  1. 1 Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
    2 Tsinghua Laboratory of Brain and Intelligence, Beijing 100084, China
    3 Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
  • Revised:2021-02-26 Online:2021-03-15 Published:2021-03-01
  • Supported by:
    The National Natural Science Foundation of China(U1736220);Tsinghua University Initiative Scientific Research Program(20197010009)


Emotional state decoding based on electroencephalography (EEG) usually regards individual emotion as a relatively static state and uses spectral power or inter-channel correlations of EEG as features.Based on recent advancement of dynamic functional connectivity analysis in the area of network neuroscience, a method called microstates of functional connectivity was designed and implemented, which clustered the inter-regional functional connectivity patterns of the brain under different emotional states to obtain representative microstates, and the temporal statistics, such as coverage and transition probability were extracted as features for emotional state decoding.Based on a widely used publicly available EEG dataset DEAP, new features in microstates of dynamic functional connectivity analysis achieved regression mean squared errors of 3.87±0.28 and 3.25±0.30 on valence and arousal respectively, which were better than those using traditional spectral power features, 4.07±0.30 (p=0.005) and 3.41±0.31 (p=0.064).The results demonstrate the feasibility of emotional state decoding based on microstates of functional connectivity and provide deeper insight into understanding the neural mechanisms of emotion.

Key words: dynamic functional connectivity, microstate, emotional state decoding, electroencephalography

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