Skip to main content
SpringerLink
Log in

Performance Comparison of Machine Learning Algorithms for EEG-Signal-Based Emotion Recognition

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10613))

Included in the following conference series:

Abstract

In this paper, we use the DEAP database to investigate emotion recognition problem. Firstly we use data clustering technique to determine four target classes of human emotional state. Then we compare two different feature extraction methods: one is wavelet transform and another is nonlinear dynamics. Furthermore, we examine the effect of feature reduction on classification performance. Finally, we compare the performance of four different classifiers, including k-nearest neighbor, naïve Bayesian, support vector machine, and random forest. The results show the effectiveness of Kernel Spectral Regression (KSR) and random forest based classifier for emotion recognition and analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Petrushin, V.: Emotion in speech: recognition and application to call centers. In: Proceedings of Artificial Neural Networks in Engineering, vol. 710 (1999)

    Google Scholar 

  2. Anderson, K., Mcowan, P.W.: A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(1), 96–105 (2006). A Publication of the IEEE Systems Man & Cybernetics Society

    Article  Google Scholar 

  3. Yin, Z., Zhao, M., Wang, Y., Yang, J., Zhang, J.: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. Comput. Methods Programs Biomed. 140, 93–110 (2017)

    Article  Google Scholar 

  4. Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)

    Article  Google Scholar 

  5. Petrantonakis, P.C., Hadjileontiadis, L.J.: 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 

  6. Li, X., Hu, B., Zhu, T., Yan, J., Zheng, F.: Towards affective learning with an EEG feedback approach. In: Proceedings of the First ACM International Workshop on Multimedia Technologies for Distance Learning. ACM (2009)

    Google Scholar 

  7. 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 

  8. Weiner, B.: Attribution, emotion, and action (1986)

    Google Scholar 

  9. Kemper, T.D.: A Social Interactional Theory of Emotions. Wiley, New York (1978)

    Google Scholar 

  10. Ekman, P.: Basic Emotions. Handbook of Cognition and Emotion, vol. 98 (1999)

    Google Scholar 

  11. Zhang, Y., Ji, X., Zhang, S.: An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci. Lett. 633, 152–157 (2016)

    Article  Google Scholar 

  12. 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 

  13. Zhang, C., Wang, H., Fu, R.: Automated detection of driver fatigue based on entropy and complexity measures. IEEE Trans. Intell. Transp. Syst. 15(1), 168–177 (2014)

    Article  Google Scholar 

  14. Vijith, V.S., Jacob, J.E., Iype, T., Gopakumar, K., Yohannan, D.G.: Epileptic seizure detection using non-linear analysis of EEG. In: International Conference on Inventive Computation Technologies (ICICT), vol. 3. IEEE (2016)

    Google Scholar 

  15. Benzy, V.K., Jasmin, E.A., Koshy, R.C.: Approximate entropy and wavelet entropy based depth of anesthesia monitoring. In: 2015 International Conference on Control Communication & Computing India (ICCC). IEEE (2015)

    Google Scholar 

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

    Article  Google Scholar 

  17. Zheng, W.: Multichannel EEG-Based emotion recognition via group sparse canonical correlation analysis. In: IEEE Transactions on Cognitive and Developmental Systems (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Peng Chen & Jianhua Zhang

Authors
  1. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Zhang .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Chen, P., Zhang, J. (2017). Performance Comparison of Machine Learning Algorithms for EEG-Signal-Based Emotion Recognition. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68600-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation