Skip to main content
SpringerLink
Log in

Optimized Echo State Network with Intrinsic Plasticity for EEG-Based Emotion Recognition

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10635))

Included in the following conference series:

Abstract

Reservoir Computing (RC) is a paradigm for efficient training of Recurrent Neural Networks (RNNs). The Echo State Network (ESN), a type of RC paradigm, has been widely used for time series forecasting. Whereas, few works exist on classification with ESN. In this paper, we shed light on the use of ESN for pattern recognition problem, i.e. emotion recognition from Electroencephalogram (EEG). We show that the reservoir with its recurrence is able to perform the feature extraction step directly from the EEG raw. Such kind of recurrence rich of nonlinearities allows the projection of the input data into a high dimensional state space. It is well known that the ESN fails due to the poor choices of its initialization. Nevertheless, we show that pretraining the ESN with the Intrinsic Plasticity (IP) rule remedies the shortcoming of randomly initialization. To validate our approach, we tested our system on the benchmark DEAP containing EEG signals of 32 subjects and the results were promising.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23, 209–237 (2009)

    Article  Google Scholar 

  2. Ekman, P.: Basic Emotions in Handbook of Cognition and Emotion. Wiley, New York (1999)

    Google Scholar 

  3. Russell, J.A.: Affective space is bipolar. J. Pers. Soc. Psychol. 37, 345–356 (1979)

    Article  Google Scholar 

  4. Mehrabian, A.: Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Current Psychol. 14, 261–292 (1996)

    Article  MathSciNet  Google Scholar 

  5. Bezine, H., Alimi, A.M., Derbel, N.: Handwriting trajectory movements controlled by a bêta-elliptic model. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, p. 1228 (2003)

    Google Scholar 

  6. Ben Moussa, S., Zahour, A., Benabdelhafid, A., Alimi, A.M.: New features using fractal multi-dimensions for generalized Arabic font recognition. Pattern Recognition Letters, vol. 31 (5), pp. 361–371 (2010)

    Google Scholar 

  7. Alimi, A.M.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002). SPEC

    Article  Google Scholar 

  8. Boubaker, H., Kherallah, M., Alimi, A.M.: New algorithm of straight or curved baseline detection for short arabic handwritten writing. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, p. 778 (2009)

    Google Scholar 

  9. Elbaati, A., Boubaker, H., Kherallah, M., Alimi, A.M., Ennaji, A., Abed, H.E.: Arabic handwriting recognition using restored stroke chronology. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, p. 411 (2009)

    Google Scholar 

  10. Slimane, F., Kanoun, S., Hennebert, J., Alimi, A.M., Ingold, R.: A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution. Pattern Recogn. Lett. 34(2), 209–218 (2013)

    Article  Google Scholar 

  11. Jaeger, H.: A tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the echo state network approach. Technical report, German National Research Center for Information Technology (2013)

    Google Scholar 

  12. Triesch, J.: A gradient rule for the plasticity of a neuron’s intrinsic excitability. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 65–70. Springer, Heidelberg (2005). doi:10.1007/11550822_11

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  14. Liu, Y., Sourina, O.: Real-time fractal-based valence level recognition from EEG. In: Gavrilova, Marina L., Tan, C.J.Kenneth, Kuijper, A. (eds.) Transactions on Computational Science XVIII. LNCS, vol. 7848, pp. 101–120. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38803-3_6

    Chapter  Google Scholar 

  15. Bahari, F., Janghorbani, A.: EEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier. In 20th Iranian Conference on Biomedical Engineering (ICBME), pp. 228–233 (2013)

    Google Scholar 

  16. Zhang, X., Hu, B., Chen, J., Moore, P.: Ontology-based context modeling for emotion recognition in an intelligent web. World Wide Web 16(4), 497–513 (2013)

    Article  Google Scholar 

  17. Zhuang, X., Rozgic, V., Crystal, M.: Compact unsupervised EEG response representation for emotion recognition. In: International Conference on Biomedical and Health Informatics (BHI), pp. 736–739 (2014)

    Google Scholar 

  18. Torres-Valencia, C., Garcia-Arias, H., Alvarez Lopez, M., Orozco-Gutierrez, A.: Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models. In: XIX Symposium on Image, Signal Processing and Artificial Vision (STSIVA), pp. 1–5 (2014)

    Google Scholar 

  19. Li, X., Zhang, P., Song, D., Yu, G., Hou, Y., Hu, B.: EEG based emotion identification using unsupervised deep feature learning. In: SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research (2015)

    Google Scholar 

  20. Zheng, W.-L., Zhu, J.-Y., Lu, B.-L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affective Comput. 8 (2017)

    Google Scholar 

  21. Bozhkov, L., Koprinkova-Hristova, P., Georgieva, P.: Reservoir computing for emotion valence discrimination from EEG signals. Neurocomputing 231, 28–40 (2017)

    Article  Google Scholar 

  22. Dhahri, H., Alimi, A.M.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: Proceedings of IEEE International Conference on Neural Networks, p. 2938 (2006)

    Google Scholar 

  23. Bouaziz, S., Dhahri, H., Alimi, A.M., Abraham, A.: A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117, 107–117 (2013)

    Article  Google Scholar 

  24. Chouikhi, N., Ammar, B., Rokabni, N., Alimi, A.M., Abraham, A.: PSO-based analysis of Echo State Network parameters for time series forecasting. Appl. Soft Comput. 55, 211–225 (2017)

    Article  Google Scholar 

  25. Chouikhi, N., Fdhila, R., Ammar, B., Rokbani, N., Alimi, A.M.: Single- and multi-objective particle swarm optimization of reservoir structure in echo state network. In: IEEE International Joint Conference on Neural Networks, Vancouver, Canada (2016)

    Google Scholar 

  26. Steil, J.J.: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. IEEE Trans. Neural Netw. 20(3), 353–364 (2007)

    Article  MATH  Google Scholar 

  27. Wardermann, M., Steil, J.J.: Intrinsic plasticity for reservoir learning algorithms. In: Verleysen, M. (ed.) Advances in Computational Intelligence and Learning (ESANN 2007), pp. 513–518 (2007)

    Google Scholar 

  28. Schrauwen, B., Wandermann, M., Verstraeten, M., Steil, J.J., Stroobandt, D.: Improving reservoirs using intrinsic plasticity. Neurocomputing 71, 1159–1171 (2008)

    Article  Google Scholar 

  29. Koprinkova-Hristova, P.: On effects of IP improvement of ESN reservoirs for reflecting of data structure. In: IEEE International Joint Conference on Neural Networks (2015)

    Google Scholar 

  30. Liu, Y., Sourina, O.: EEG databases for emotion recognition. In: International Conference on Cyberworlds (2013)

    Google Scholar 

Download references

Acknowledgment

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

Author information

Authors and Affiliations

  1. REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Rahma Fourati, Boudour Ammar & Adel M. Alimi

  2. Research Units of Mathematics and Applications UR13ES47, Department of Mathematics, Faculty of Sciences of Bizerta, University of Carthage, Zarzouna, 7021, Bizerta, Tunisia

    Chaouki Aouiti

  3. CICEI: Innovation Center for the Information Society, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Javier Sanchez-Medina

Authors
  1. Rahma Fourati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boudour Ammar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaouki Aouiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Sanchez-Medina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adel M. Alimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahma Fourati .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fourati, R., Ammar, B., Aouiti, C., Sanchez-Medina, J., Alimi, A.M. (2017). Optimized Echo State Network with Intrinsic Plasticity for EEG-Based Emotion Recognition. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_73

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70096-0_73

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70095-3

  • Online ISBN: 978-3-319-70096-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation