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

  • Rahma Fourati
  • Boudour Ammar
  • Chaouki Aouiti
  • Javier Sanchez-Medina
  • Adel M. Alimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


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.


Echo state network Intrinsic plasticity Feature extraction Classification Electroencephalogram Emotion recognition 



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.


  1. 1.
    Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23, 209–237 (2009)CrossRefGoogle Scholar
  2. 2.
    Ekman, P.: Basic Emotions in Handbook of Cognition and Emotion. Wiley, New York (1999)Google Scholar
  3. 3.
    Russell, J.A.: Affective space is bipolar. J. Pers. Soc. Psychol. 37, 345–356 (1979)CrossRefGoogle Scholar
  4. 4.
    Mehrabian, A.: Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Current Psychol. 14, 261–292 (1996)CrossRefMathSciNetGoogle Scholar
  5. 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. 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. 7.
    Alimi, A.M.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002). SPECCrossRefGoogle Scholar
  8. 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. 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. 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)CrossRefGoogle Scholar
  11. 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. 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 CrossRefGoogle Scholar
  13. 13.
    Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affective Comput. 3, 18–31 (2012)CrossRefGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. 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. 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)CrossRefGoogle Scholar
  17. 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. 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. 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. 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. 21.
    Bozhkov, L., Koprinkova-Hristova, P., Georgieva, P.: Reservoir computing for emotion valence discrimination from EEG signals. Neurocomputing 231, 28–40 (2017)CrossRefGoogle Scholar
  22. 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. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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. 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)CrossRefzbMATHGoogle Scholar
  27. 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. 28.
    Schrauwen, B., Wandermann, M., Verstraeten, M., Steil, J.J., Stroobandt, D.: Improving reservoirs using intrinsic plasticity. Neurocomputing 71, 1159–1171 (2008)CrossRefGoogle Scholar
  29. 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. 30.
    Liu, Y., Sourina, O.: EEG databases for emotion recognition. In: International Conference on Cyberworlds (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rahma Fourati
    • 1
  • Boudour Ammar
    • 1
  • Chaouki Aouiti
    • 2
  • Javier Sanchez-Medina
    • 3
  • Adel M. Alimi
    • 1
  1. 1.REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS)University of SfaxSfaxTunisia
  2. 2.Research Units of Mathematics and Applications UR13ES47, Department of Mathematics, Faculty of Sciences of BizertaUniversity of CarthageBizertaTunisia
  3. 3.CICEI: Innovation Center for the Information SocietyUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

Personalised recommendations