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Emotion Recognition System Based on EEG Signal Analysis Using Auditory Stimulation: Experimental Design

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HCI International 2019 - Posters (HCII 2019)

Abstract

In this document the design of an emotion recognition system based on the EEG signals analysis based on auditory stimulation is proposed. Here, an auditory emotion recognition protocol using the International Affective Digitalized Sounds (IADS) second edition database is introduced, in which the database is divided into three groups: Negative, Positive and Neutral sonorous stimuli according to their normative mean valence and arousal ratings. The protocol was implemented through the psychopy3 stimulation libraries, and the signal acquisition is made using the Emotiv EPOC+ device through a software developed in the python environment. The stimulation protocol and the acquisition process are synchronized through pulses allowing to carryout stimulus register and to control the experiment.

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References

  1. Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695–729 (2005)

    Article  Google Scholar 

  2. Llinás, R.R.: El cerebro y el mito del yo: el papel de las neuronas en el pensamiento y el comportamiento humanos. Editorial Norma (2003)

    Google Scholar 

  3. Liu, Y.J., Yu, M., Zhao, G., Song, J., Ge, Y., Shi, Y.: Real-time movie-induced discrete emotion recognition from EEG signals. IEEE Trans. Affect. Comput. 9(4), 550–562 (2018)

    Article  Google Scholar 

  4. Al-Nafjan, A., Hosny, M., Al-Ohali, Y., Al-Wabil, A.: Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Appl. Sci. 7(12), 1239 (2017)

    Article  Google Scholar 

  5. Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Article  Google Scholar 

  6. Sourina, O., Liu, Y., Nguyen, M.K.: Real-time EEG-based emotion recognition for music therapy. J. Multimodal User Interfaces 5(1–2), 27–35 (2012)

    Article  Google Scholar 

  7. Teo, J., Chia, J.T.: Deep neural classifiers for EEG-based emotion recognition in immersive environments. In: 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), pp. 1–6. IEEE (2018)

    Google Scholar 

  8. Sanei, S.: Adaptive Processing of Brain Signals. Wiley, Hoboken (2013)

    Book  Google Scholar 

  9. Amari, S., et al.: The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (2003)

    Google Scholar 

  10. Luck, S.J.: An Introduction to the Event-Related Potential Technique. MIT Press, Cambridge (2014)

    Google Scholar 

  11. Peirce, J., et al.: PsychoPy2: experiments in behavior made easy. Behav. Res. Methods 51, 1–9 (2019)

    Article  Google Scholar 

  12. Bradley, M., Lang, P.J.: The International affective digitized sounds (IADS): stimuli, instruction manual and affective ratings. NIMH Center for the Study of Emotion and Attention (1999)

    Google Scholar 

  13. Bradley, M.M., Lang, P.J.: The international affective digitized sounds (IADS-2): affective ratings of sounds and instruction manual. Technical report B-3. University of Florida, Gainesville, FL (2007)

    Google Scholar 

  14. Mendez-Alegria, R., Yenny, C.C., Granollers, T.: Rueda de emociones de ginebra+: instrumento para la evaluación emocional de los usuarios mientras participan en una evaluación de sistemas interactivos. Rev. Ing. Dyna, vol. En prepara (2015)

    Google Scholar 

  15. Homan, R.W., Herman, J., Purdy, P.: Cerebral location of international 10–20 system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66(4), 376–382 (1987)

    Article  Google Scholar 

  16. Emotiv (2019)

    Google Scholar 

  17. Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015)

    Article  Google Scholar 

  18. Hurtado-Rincón, J.V., Martínez-Vargas, J.D., Rojas-Jaramillo, S., Giraldo, E., Castellanos-Dominguez, G.: Identification of relevant inter-channel EEG connectivity patterns: a kernel-based supervised approach. In: Ascoli, G.A., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds.) BIH 2016. LNCS (LNAI), vol. 9919, pp. 14–23. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47103-7_2

    Chapter  Google Scholar 

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Correspondence to Catalina Aguirre-Grisales .

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Aguirre-Grisales, C., Gaviria-Cardenas, E., Castro-Londoño, V.H., Torres-Cardona, H.F., Rodriguez-Sotelo, J.L. (2019). Emotion Recognition System Based on EEG Signal Analysis Using Auditory Stimulation: Experimental Design. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_31

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  • DOI: https://doi.org/10.1007/978-3-030-23528-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23527-7

  • Online ISBN: 978-3-030-23528-4

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