Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings

Research Article

Correlation Study of Emotional Brain Areas Induced by Video

  • @INPROCEEDINGS{10.1007/978-3-030-43215-7_14,
        author={Huiping Jiang and Zequn Wang and XinKai Gui and GuoSheng Yang},
        title={Correlation Study of Emotional Brain Areas Induced by Video},
        proceedings={Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings},
        proceedings_a={TRIDENTCOM},
        year={2020},
        month={3},
        keywords={Brain areas EEG Correlation Emotion},
        doi={10.1007/978-3-030-43215-7_14}
    }
    
  • Huiping Jiang
    Zequn Wang
    XinKai Gui
    GuoSheng Yang
    Year: 2020
    Correlation Study of Emotional Brain Areas Induced by Video
    TRIDENTCOM
    Springer
    DOI: 10.1007/978-3-030-43215-7_14
Huiping Jiang1,*, Zequn Wang1, XinKai Gui1, GuoSheng Yang1,*
  • 1: Minzu University of China
*Contact email: jianghp@muc.edu.cn, Yangguosheng@muc.edu.cn

Abstract

Emotions are physiological phenomena caused by complex cognitive activities. With the in-depth study of artificial intelligence and brain mechanism of emotion, affective computing has become a hot topic in computer science. In this paper, we used the existed emotional classification model based on electroencephalograph (EEG) to calculate the accuracy of emotion classification in 4 brain areas roughly sorted into frontal, parietal, occipital, and temporal lobes in terms of brain functional division, to infer the correlation between the emotion and 4 brain areas based on the accuracy rate of the emotion recognition. The result shows that the brain areas most related to emotions are located in the frontal and temporal lobes, which is consistent with the brain mechanism of emotional processing. This research work will provide a good guideline for selecting the most relevant electrodes with emotions to enhance the accuracy of emotion recognition based on EEG.