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A Survey of Methods and Performances for EEG-Based Emotion Recognition

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Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 552))

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Abstract

EEG-based Emotion Recognition is regarded as a new field of affective computing researching, though presenting many challenging issues concerning the manner how emotions are elicited, and the different techniques used for features extraction and their ability to achieve high classification performance. This article reviews the Emotion Recognition techniques applied and developed recently. In general terms, emotion evocation based on audio-visual stimuli, features extraction techniques and classifiers are surveyed in the field of Emotion Recognition. A comparative table of recent researches is also conducted. Based on a discussion of previous studies, our proposed architecture is presented as future work.

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References

  1. Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based Emotion Recognition and its Applications (2011)

    Google Scholar 

  2. Jatupaiboon, N., Pan-ngum, S., Israsena, P.: Real-Time EEG-based happiness detection system. Sci. World J. (2013)

    Google Scholar 

  3. Murugappan, M., Sazali, Y., Hazry, D.: Lifting schema for humanemotion recognition using EEG. In: International Symposium on Information Technology (2008)

    Google Scholar 

  4. Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2), 186–197 (2010)

    Article  Google Scholar 

  5. Kothe, C., Onton, J., Makeig, S.: Emotion recognition from EEG during self-paced emotional imagery. In: Affective Computing and Intelligent Interaction (2013)

    Google Scholar 

  6. Lin, Y.P., Wang, C., Wu, T.L., Jeng, S.K., Chen, J.H.: EEG-based emotion recognition in music listening: a comparison of schemes for multiclass support vector machine. In: IEEE ICASSP (2009)

    Google Scholar 

  7. Schuster, T., Gruss, S., Kessler, H., Scheck, A., Hoffmann, H., Traue, H.: EEG: pattern classification during emotional picture processing. In: 3rd International Conference on PErvasive Technologies Related to Assistive Environments, vol. 67 (2010)

    Google Scholar 

  8. Jatupaiboon, N., Pan-ngum, S., Israsena, P.: Emotion classification using minimal EEG channels and frequency bands. In: The 10th International Joint Conference on Computer Science and Software Engineering, May 2013

    Google Scholar 

  9. Anh, V.H., Van, M.N., Ha, B.B., Quyet, T.H.: A real-time model based support vector machine for emotion recognition through EEG. In: ICCAIS (2012)

    Google Scholar 

  10. Haiyan, X., Konstantinos, N.: Affect recognition using EEG signal. In: MMSP Conference (2012)

    Google Scholar 

  11. Bastos-Filho, T.F., Ferreira, A., Atencio, A.C., Arjunan, S., Kumar, D.: Evaluation of feature extraction techniques in emotional state recognition. In: The 4th International Conference on Intelligent Human Computer Interaction (IHCI) (2012)

    Google Scholar 

  12. Bos, D.O.: EEG-based Emotion Recognition: The Influence of Visual and Auditory Stimuli, pp. 1–17 (2006)

    Google Scholar 

  13. Benbadis, S.R., Rielo, D.A., Talavera, F., Alvarez, N.: EEG Artifacts (2015)

    Google Scholar 

  14. Anh, V.H., Van, M.N., Ha, B.B., Quyet, T.H.: A real-time model based support vector machine for emotion recognition through EEG. In: ICCAIS 2012 (2012)

    Google Scholar 

  15. Lin, Y., Wang, C.-H., Jung, T., Wu, T.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)

    Article  Google Scholar 

  16. Petrantonakis, P.C., Hadjileontiadis, L.J.: EEG-based emotion recognition using hybrid filtering and higher order crossings. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (2009)

    Google Scholar 

  17. Petrantonakis, P., Hadjileontiadis, L.: A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition. IEEE Trans. Inf. Technol. Biomed. 15(5), 737–746 (2011)

    Article  Google Scholar 

  18. Schaaff, K., Schultz, T.: Towards an EEG-based emotion recognizer for humanoid robots. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 792–796 (2009)

    Google Scholar 

  19. Mampusti, E.T., Ng, J.S., Quinto, J.J.I., Teng, G.L., Suarez, M.T.C., Trogo, R.S.: Measuring academic affective states of students via brainwave signals. In: 3rd International Conference on Knowledge and Systems Engineering (2011)

    Google Scholar 

  20. Sourina, O., Liu, Y.: A fractal-based algorithm of Emotion Recognition from EEG using Arousal-Valence Model. In: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, January 2011

    Google Scholar 

  21. Zhang, Q., Lee, M.: Analysis of positive and negative emotions in natural scene using brain activity and GIST. Neurocomputing 72, 1302–1306 (2009)

    Article  Google Scholar 

  22. Kulish, V., Sourin, A., Sourina, O.: Analysis and visualization of human electroencephalograms seen as fractal time series. J. Mech. Med. Biol. World Sci. 26(2), 175–188 (2006)

    Article  Google Scholar 

  23. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International Affective Picture System (IAPS): technical manual and affective ratings. The Center for Research in Psychophysiology. University of Florida, USA (2005)

    Google Scholar 

  24. Lui, S., Meng, J., Zhang, D., Yang, J., Zhao, X., He, F., Qi, H., Ming, D.: Emotion recognition based on EEG changes in movie viewing. In: 7th Annual International IEEE EMBS Conference on Neural Engineering (2015)

    Google Scholar 

  25. Hou, C.L., Mountstephens, J., Teo, J.: EEG-based recognition of positive and negative emotions using for pleasant vs. unpleasant images. Int. J. Recent Adv. Multi. Res. 02(06), 181–485 (2015)

    Google Scholar 

  26. Lokannavar, S., Lahane, P., Gangurde, A., Chidre, P.: Emotion recognition using EEG signals. Int. J. Adv. Res. Comput. Commun. Eng. 5(5), 54–56 (2015)

    Google Scholar 

  27. Atkinson, J., Campos, D.: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Int. J. Expert Syst. Appl. 47(C), 35–41 (2016)

    Article  Google Scholar 

  28. Zheng, W.-L., Lu, B.-L.: Investigation 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 

  29. Emotiv EPOC/EPOC+. https://emotiv.com/epoc.php

  30. Kulish, V., Sourin, A., Sourina, O.: Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput. Biol. Med. 36, 291–302 (2006)

    Article  Google Scholar 

  31. Russell, J.A.: A circumplex model of affect. J. PSP 39(6), 1161–1178 (1980)

    Google Scholar 

  32. Collura, T.F.: The Measurement, Interpretation, and Use of EEG Frequency Bands (1997)

    Google Scholar 

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Acknowledgment

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Authors and Affiliations

  1. REGIM: REsearch Groups in Intelligent Machines, Sfax, Tunisia

    Asma Baghdadi, Yassine Aribi & Adel M. Alimi

  2. University of Sfax, National School of Engineers, BP 1173, 3038, Sfax, Tunisia

    Yassine Aribi & Adel M. Alimi

Authors
  1. Asma Baghdadi
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  2. Yassine Aribi
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  3. Adel M. Alimi
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Corresponding author

Correspondence to Asma Baghdadi .

Editor information

Editors and Affiliations

  1. (MIR Labs), Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

  4. Technopolis Rabat-Shore Rocade, International University of Rabat, Sala el Jadida, Morocco

    Ghita Mezzour

  5. Inst Applied Dept of Electronics, Taffala, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Baghdadi, A., Aribi, Y., Alimi, A.M. (2017). A Survey of Methods and Performances for EEG-Based Emotion Recognition. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-52941-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52940-0

  • Online ISBN: 978-3-319-52941-7

  • eBook Packages: EngineeringEngineering (R0)

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