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Does Facial Expression Accurately Reveal True Emotion? Evidence from EEG Signal

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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1074))

Abstract

Facial expression is one of the ways in which human expresses emotion. However, there has been evidence that facial expression does not always accurately reflect inner emotions, leading to the shaking of theories about human basic universal emotions. In this study, we conduct an experiment to collect facial expression data and EEG signals of participant. The results indicate that not all kinds of basic emotion were expressed in facial expressions under the experiment environment. On the other hand, brain signals have proven to be a highly reliable tool for emotion recognition task. This study shows the problems faced by facial emotion recognition systems and proposes future works to improve the efficiency of those systems.

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References

  1. Gannouni, S., Aledaily, A., Belwafi, K., Aboalsamh, H.: Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification. Sci. Rep. 11(1), 1–17 (2021)

    Article  Google Scholar 

  2. Meiselman, H.L. (Ed.): Emotion Measurement. Woodhead publishing (2016)

    Google Scholar 

  3. Barrett, L.F., Lewis, M., Haviland-Jones, J.M. (Eds.): Handbook of Emotions. Guilford Publications (2016)

    Google Scholar 

  4. Panksepp, J: Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford university press (2004)

    Google Scholar 

  5. Damasio, A.R.: Emotion in the perspective of an integrated nervous system. Brain Res. Rev. 26(2–3), 83–86 (1998)

    Article  Google Scholar 

  6. Ekman, P.E., Davidson, R.J: The Nature of Emotion: Fundamental Questions. Oxford University Press (1994)

    Google Scholar 

  7. Cabanac, M.: What is emotion? Behav. Proc. 60(2), 69–83 (2002)

    Article  Google Scholar 

  8. Schacter, D., Gilbert, D., Wegner, D., Hood, B.M.: Psychology: European Edition. Macmillan International Higher Education (2011)

    Google Scholar 

  9. Pinker, S.: How the Mind Works, vol. 524. Norton, New York (1997)

    Google Scholar 

  10. Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)

    Article  Google Scholar 

  11. Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognit. 36, 259–275 (2003)

    Article  MATH  Google Scholar 

  12. Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Wang, X.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multimedia 12(7), 682–691 (2010)

    Article  Google Scholar 

  13. Ekman, P., Sorenson, E.R., Friesen, W.V.: Pan-cultural elements in facial displays of emotion. Science 164(3875), 86–88 (1969)

    Article  Google Scholar 

  14. Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)

    Article  Google Scholar 

  15. Barrett, L.F., Adolphs, R., Marsella, S., Martinez, A.M., Pollak, S.D.: Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychol. Sci. Public Interes. 20(1), 1–68 (2019)

    Article  Google Scholar 

  16. Heaven, D.: Why faces don’t always tell the truth about feelings. Nature 578(7796), 502–505 (2020)

    Article  Google Scholar 

  17. Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384 (1993)

    Article  Google Scholar 

  18. Gu, S., Wang, F., Patel, N.P., Bourgeois, J.A., Huang, J.H.: A model for basic emotions using observations of behavior in drosophila. Front. Psychol. 781 (2019)

    Google Scholar 

  19. Chen, C., Crivelli, C., Garrod, O.G., Schyns, P.G., Fernández-Dols, J.M., Jack, R.E.: Distinct facial expressions represent pain and pleasure across cultures. Proc. Natl. Acad. Sci. 115(43), E10013–E10021 (2018)

    Article  Google Scholar 

  20. Crivelli, C., Russell, J.A., Jarillo, S., Fernández-Dols, J.M.: Recognizing spontaneous facial expressions of emotion in a small-scale society of Papua new Guinea. Emotion 17(2), 337 (2017)

    Article  Google Scholar 

  21. Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)

    Article  Google Scholar 

  22. Toisoul, A., Kossaifi, J., Bulat, A., Tzimiropoulos, G., Pantic, M.: Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat. Mach. Intell. 3(1), 42–50 (2021)

    Article  Google Scholar 

  23. Russell, J.A., Carroll, J.M.: On the bipolarity of positive and negative affect. Psychol. Bull. 125(1), 3 (1999)

    Article  Google Scholar 

  24. Watson, D., Clark, L.A.: Affects separable and inseparable: on the hierarchical arrangement of the negative affects. J. Pers. Soc. Psychol. 62(3), 489 (1992)

    Article  Google Scholar 

  25. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)

    Article  Google Scholar 

  26. Rubin, D.C., Talarico, J.M.: A comparison of dimensional models of emotion: evidence from emotions, prototypical events, autobiographical memories, and words. Memory 17(8), 802–808 (2009)

    Article  Google Scholar 

  27. Remington, N.A., Fabrigar, L.R., Visser, P.S.: Reexamining the circumplex model of affect. J. Pers. Soc. Psychol. 79(2), 286 (2000)

    Article  Google Scholar 

  28. Hamann, S.: Mapping discrete and dimensional emotions onto the brain: controversies and consensus. Trends Cogn. Sci. 16(9), 458–466 (2012)

    Article  Google Scholar 

  29. Abhang, P.A., Gawali, B.W., Mehrotra, S.C.: Technological basics of EEG recording and operation of apparatus. In: Introduction to EEG-and Speech-Based Emotion Recognition, pp. 19−50 (2016)

    Google Scholar 

  30. Jenke, R., Peer, A., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3), 327–339 (2014)

    Article  Google Scholar 

  31. Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)

    Article  Google Scholar 

  32. Bos, D.O.: EEG-based emotion recognition. Influ. Vis. Audit. Stimuli 56(3), 1–17 (2006)

    Google Scholar 

  33. Schubring, D., Schupp, H.T.: Emotion and brain oscillations: high arousal is associated with decreases in alpha-and lower beta-band power. Cereb. Cortex 31(3), 1597–1608 (2021)

    Article  Google Scholar 

  34. Hwang, S., Jebelli, H., Choi, B., Choi, M., Lee, S.: Measuring workers’ emotional state during construction tasks using wearable EEG. J. Constr. Eng. Manag. 144(7), 04018050 (2018)

    Article  Google Scholar 

  35. Rusinov, V.S.: Electrophysiology of the Central Nervous System. Springer Science & Business Media, (2012)

    Google Scholar 

  36. Blaiech, H., Neji, M., Wali, A., Alimi, A.M.: Emotion recognition by analysis of EEG signals. In: 13th International Conference on Hybrid Intelligent Systems (HIS 2013), pp. 312−318. IEEE (2013, December)

    Google Scholar 

  37. Ramirez, R., Vamvakousis, Z.: Detecting emotion from EEG signals using the emotive epoc device. In: International Conference on Brain Informatics, pp. 175−184. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  38. Kim, H.S., Lee, J.H.: Neuro-scientific approach to fashion visual merchandising-comparison of brain activation to positive/negative VM in fashion store using fNIRS. J. Korean Soc. Cloth. Text. 41(2), 254–265 (2017)

    Article  Google Scholar 

  39. Benitez, D.S., Toscano, S., Silva, A.: On the use of the Emotiv EPOC neuroheadset as a low cost alternative for EEG signal acquisition. In: 2016 IEEE Colombian Conference on Communications and Computing (COLCOM), IEEE, 1−6 April 2016

    Google Scholar 

  40. General Assembly of the World Medical Association: World medical association declaration of helsinki: ethical principles for medical research involving human subjects. J. Am. Coll. E Dent. 81(3), 14–18 (2014)

    Google Scholar 

  41. Cowen, A.S., Keltner, D.: Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proc. Natl. Acad. Sci. 114(38), E7900–E7909 (2017)

    Article  Google Scholar 

  42. Available: https://www.emotiv.com/

  43. Brunner, C., Delorme, A., Makeig, S.: Eeglaban open source matlab toolbox for electrophysiological research. Biomed. Eng./Biomed. Tech. 58(SI-1-Track-G), 000010151520134182 (2013)

    Google Scholar 

  44. Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav. Brain Funct. 7(1), 1–15 (2011)

    Article  Google Scholar 

  45. Benitez-Quiroz, C.F., Srinivasan, R., Martinez, A.M.: Facial color is an efficient mechanism to visually transmit emotion. Proc. Natl. Acad. Sci. 115(14), 3581–3586 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Business Administration, Soongsil University, Seoul, South Korea

    Huy Tung Phuong & Gwangyong Gim

  2. Department of IT Policy and Management, Soongsil University, Seoul, South Korea

    Yangyoung Kun

  3. Management Planning HQ, Soongsil University, Seoul, South Korea

    Jisook Kim

Authors
  1. Huy Tung Phuong
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  2. Yangyoung Kun
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  3. Jisook Kim
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  4. Gwangyong Gim
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Corresponding author

Correspondence to Gwangyong Gim .

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Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mt. Pleasant, MI, USA

    Roger Lee

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Phuong, H.T., Kun, Y., Kim, J., Gim, G. (2023). Does Facial Expression Accurately Reveal True Emotion? Evidence from EEG Signal. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2022. Studies in Computational Intelligence, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-031-19604-1_14

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