Correlation Study of Emotional Brain Areas Induced by Video

  • Huiping JiangEmail author
  • Zequn Wang
  • XinKai Gui
  • GuoSheng Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 309)


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.


Brain areas EEG Correlation Emotion 



Huiping Jiang has been supported by the National Nature Science Foundation of China (NO. 61503423). And this work has been supported in part by the Leading Talent Program of State Ethnic Affairs Commission, and Double First-class Special Funding of MUC.


  1. 1.
    Picard, R.W.: Affective Computing. MIT Press, London (1997)Google Scholar
  2. 2.
    Nie, D., Wang, X.W., Duan, R.N., Lu, B.L.: A survey on EEG based emotion recognition. Chin. J. Biomed. Eng. 31(4), 595–606 (2012)Google Scholar
  3. 3.
    Upadhyay, D.: Classification of EEG signals under different mental tasks using wavelet transform and neural network with one step secant algorithm. Int. J. Sci. Eng. Technol. 2(4), 256–259 (2013)Google Scholar
  4. 4.
    Kim, B.K., Lee, E.C., Suhng, B.M.: Feature extraction using FFT for banknotes recognition in a variety of lighting conditions. In: International Conference on Control, pp. 698–700 (2014)Google Scholar
  5. 5.
    Duan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for EEG-based emotion classification. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 81–84. IEEE (2013)Google Scholar
  6. 6.
    Nie, D., Wang, X.W., Shi, L.C., Lu, B.L.: EEG-based emotion recognition during watching movies. In: Proceeding of the 5th International IEEE EMBS Conference on Neural Engineering, pp. 667–670 (2011)Google Scholar
  7. 7.
    Murugappan, M., Ramachandran, N., Sazali, Y.: Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 2(4), 390–396 (2010)CrossRefGoogle Scholar
  8. 8.
    Gupta, A., Agrawal, R.K., Kaur, B.: Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods. Soft. Comput. 19(10), 2799–2812 (2015)CrossRefGoogle Scholar
  9. 9.
    Subasi, A., Gursoy, M.I.: Comparison of PCA, ICA and LDA in EEG signal classification using DWT and SVM. Exp. Syst. Appl. 37(37), 8659–8666 (2010)CrossRefGoogle Scholar
  10. 10.
    Yanagimoto, M., Sugimoto, C.: Recognition of persisting emotional valence from EEG using convolutional neural networks. In: IEEE International Workshop on Computational Intelligence & Applications, pp. 27–32 (2017)Google Scholar
  11. 11.
    Baghaee, S., Onak, O.N., Ulusoy, I.: Inferring brain effective connectivity via DBN and EEG time series data. In: International Scientific Conference of Iranian Academics in Turkey (2014)Google Scholar
  12. 12.
    Seijdel, N., Ramakrishnan, K., Losch, M.: Overlap in performance of CNN’s, human behavior and EEG classification. J. Vis. 16(12), 501 (2016)CrossRefGoogle Scholar
  13. 13.
    Zheng, W.L., Zhu, J.Y., Peng, Y., et al.: EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2014)Google Scholar
  14. 14.
    Darwin, C., Ekman, P.: The Expression of the Emotions in Man and Animals. Oxford University Press, New York (1872/1998)Google Scholar
  15. 15.
    James, W.: What is an emotion? Mind 9(34), 188–205 (1884)CrossRefGoogle Scholar
  16. 16.
    Izard, C.E.: The many meanings/aspects of emotion: definitions, functions, activation, and regulation. Emot. Rev. 2(4), 363–370 (2010)CrossRefGoogle Scholar
  17. 17.
    LeDoux, J.: Emotional networks and motor control: a fearful view. Prog. Brain Res. 107, 437–446 (1996)CrossRefGoogle Scholar
  18. 18.
    Sturm, V.E., Yokoyama, J.S., Seeley, W.W., et al.: Heightened emotional contagion in mild cognitive impairment and Alzheimer’s disease is associated with temporal lobe degeneration. Proc. Natl. Acad. Sci. 110(24), 9944–9949 (2013)CrossRefGoogle Scholar
  19. 19.
    Schmidt, L.A., Trainor, L.J.: Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn. Emot. 15(4), 487–500 (2001)CrossRefGoogle Scholar
  20. 20.
    Lu, Y., Jiang, H., Liu, W.: Classification of EEG Signal by STFT-CNN Framework: identification of right-/left-hand Motor Imagination in BCI Systems. In: 7th International Conference on Computer Engineering and Networks, Shanghai, China, 22–23 July 2017 (2017)Google Scholar
  21. 21.
    Zhou, Z.: Research on EEG signal characteristic representation in emotion recognition. Master Thesis, Minzu University of China (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  1. 1.Brain Cognitive Computing Lab, School of Information EngineeringMinzu University of ChinaBeijingChina

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