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EEG Based Classification of Human Emotions Using Discrete Wavelet Transform

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IT Convergence and Security 2017

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 450))

Abstract

Electroencephalography is widely used to study the dynamics of neural information processing in the brain and to diagnose brain disorder and cognitive processes. In this paper, we proposed EEG based emotion recognition system using Discrete Wavelet Transformation. A set of highly significant features based on wavelets coefficients has been extracted which also includes modified wavelet energy features. In order to minimize redundancy and maximize relevancy among features, mRMR algorithm is significantly applied for feature selection. Multi class Support Vector Machine is used to perform classification of four classes of human emotions. EEG recordings of “DEAP” database are used in this experiment. The proposed approach shows significant performance compared to existing algorithms.

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References

  1. Bilal, M., Kang, S.-G.: An authentication protocol for future sensor networks. Sensors 17(5), 979 (2017)

    Article  Google Scholar 

  2. Ekman, P.: Emotions Revealed. Times Books (2003)

    Google Scholar 

  3. Zhang, L., Tjondronegoro, D.: Facial expression recognition using facial movement features. IEEE Trans. Aff. Comput. 2, 219–229 (2011)

    Article  Google Scholar 

  4. Wang, K., Ning, A., Li, B.N., Zhang, Y.: Speech emotion recognition using Fourier parameters. IEEE Trans. Aff. Comput. 6, 69–75 (2015)

    Article  Google Scholar 

  5. Anttonen. J., Surakka, V.: Emotions and heart rate while sitting on a chair. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 491–499 (2005)

    Google Scholar 

  6. Jones, C.M., Troen, T.: Biometric valence and arousal recognition. In: Proceedings of the 19th Australasian Conference on Computer-Human Interaction, pp. 191–194 (2007)

    Google Scholar 

  7. Posner et al.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17 (2005)

    Google Scholar 

  8. Lang, P.J., Bradley, M. M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical repory A-8 (2008)

    Google Scholar 

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

    Google Scholar 

  10. Petrantonakis et al.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2) 186–197 (2010)

    Google Scholar 

  11. Murugappan et al.: Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 3(4), 390 (2010)

    Google Scholar 

  12. Sourina, O., Yisi, L.: A fractal-based algorithm of emotion recognition from EEG using arousal-valence model. BIOSIGNALS (2011)

    Google Scholar 

  13. Lan, T., et al.: Estimating cognitive state using EEG signals. In: 2005 13th European IEEE Signal Processing Conference (2005)

    Google Scholar 

  14. Koelstra, S., et al.: Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. In: Proceeding of the International Conference on Brain Informatics, BI 2010, Toronto, Canada, pp. 89–100 (2010)

    Google Scholar 

  15. Wijeratne, U., et al.: Intelligent emotion recognition system using electroencephalography and active shape models. In: Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012, pp. 636–641 (2012)

    Google Scholar 

  16. Khalili, Z., Moradi, M. H.: Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, pp. 1571–1575 (2009)

    Google Scholar 

  17. Soleymani, M., et al.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Aff. Comput. 3(1), 42–55 (2012)

    Article  MathSciNet  Google Scholar 

  18. Abadi, M., et al.: DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans. Aff. Comput. 6(3), 209–222 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Daimi, S.N., Saha, G.: Classification of emotions induced by music videos and correlation with participants rating. Expert Syst. Appl. 41(13), 6057–6065 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government(MSIP) (Development of SW fused Wearable Device Module and Flexible SW Application Platform for the integrated Management of Human Activity).

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Authors and Affiliations

  1. Korea University of Science and Technology, Daejeon, Korea

    Muhammad Zubair

  2. Electronics and Telecommunication Research Institute, Daejeon, Korea

    Changwoo Yoon

Authors
  1. Muhammad Zubair
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  2. Changwoo Yoon
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Corresponding author

Correspondence to Muhammad Zubair .

Editor information

Editors and Affiliations

  1. Kyonggi University, Seongnam-si, Kyonggi-do, Korea (Republic of)

    Kuinam J. Kim

  2. Computer Science, Namseoul University, Cheonan, Chungcheongnam-do, Korea (Republic of)

    Hyuncheol Kim

  3. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Nakhoon Baek

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Zubair, M., Yoon, C. (2018). EEG Based Classification of Human Emotions Using Discrete Wavelet Transform. In: Kim, K., Kim, H., Baek, N. (eds) IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, vol 450. Springer, Singapore. https://doi.org/10.1007/978-981-10-6454-8_3

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  • DOI: https://doi.org/10.1007/978-981-10-6454-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6453-1

  • Online ISBN: 978-981-10-6454-8

  • eBook Packages: EngineeringEngineering (R0)

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