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A novel EEG-based approach to classify emotions through phase space dynamics

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Abstract

Emotion recognition has drawn a great deal of attention in brain computer interface field. Researches have come to the conclusion that nonlinear features are more successful in emotion classification because of non-stationary and chaotic behavior of biological signals, especially electroencephalogram (EEG). In this study, a new method based on phase space dynamics and Poincare sections is introduced to classify emotions. This transformation quantifies the phase space and represents its characteristics in a new state space which can simply reflect the signal behavior. Not only are the proposed state space and quantifiers effective to appropriately describe nonlinear signal dynamics, but also this method successfully works in real-world applications like EEG-based emotion recognition. In this work, EEGs are transformed into the new state space and Poincare intersections are considered as features with the aim of classifying EEGs into four emotional groups based on arousal and valence values. A reliable dataset is used, and classification accuracy is about 82%. As results suggest, the proposed method and features are efficient in this task in comparison with previous studies employing the same dataset.

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References

  1. 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 

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

    Article  Google Scholar 

  3. Fan, M., Chou, C.A.: Recognizing affective state patterns using regularized learning with nonlinear dynamical features of EEG. In: IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE, Las Vegas, NV, March 4–7, pp. 137–140 (2018)

  4. Tripathi, S., Acharya, Sh., Sharma, R.D., Mittal, S., Bhattacharya, S.: Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In: Twenty-Ninth IAAI Conference, AAAI Publications, Feb 8, pp. 4746–4752 (2017)

  5. Mihandoost, S., Amirani, M.C.: EEG signal analysis using spectral correlation function and GARCH model. Signal Image Video Process. 9(6), 1461–1472 (2015)

    Article  Google Scholar 

  6. Goshvarpour, A., Abbasi, A., Goshvarpour, A., Daneshvar, S.: Discrimination between different emotional states based on the chaotic behavior of galvanic skin responses. Signal Image Video Process. 11(7), 1347–1355 (2017)

    Article  Google Scholar 

  7. Naji, M., Firoozabadi, M., Azadfallah, P.: Emotion classification during music listening from forehead biosignals. Signal Image Video Process. 9(6), 1365–1375 (2015)

    Article  Google Scholar 

  8. Liu, Y., Sourina, O., Nguyen, M.K.: Real-Time EEG-Based Emotion Recognition and Its Applications, Transactions on Computational Science XII (Lecture Notes in Computer Science). vol. 6670, pp. 256–277. Springer, Berlin (2011)

  9. Mekler, A., Gorbunov, I., Gavrilov, V.: Systemic processes in the brain: the EEG study on the emotions of different hierarchical levels and signs. Int. J. Psychophysiol. 94(2), 191–191 (2014)

    Article  Google Scholar 

  10. Soroush, M.Z., Maghooli, K., Setarehdan, S.K., Nasrabadi, A.M.: A novel method of EEG-based emotion recognition using nonlinear features variability and DempsterShafer theory. Biomed. Eng. Appl. Basis Commun. 30(04), 1850026 (2018)

    Article  Google Scholar 

  11. Soroush, M.Z., Maghooli, K., Setarehdan, S.K., Nasrabadi, A.M.: A novel approach to emotion recognition using local subset feature selection and modified Dempster–Shafer theory. Behav. Brain Funct. 14(1), 17 (2018)

    Article  Google Scholar 

  12. Soroush, M.Z., Maghooli, K., Setarehdan, S.K., Nasrabadi, A.M.: A Review on EEG signals based emotion recognition. Int. Clin. Neurosci. J. 4(4), 118–129 (2017)

    Article  Google Scholar 

  13. Soroush, M.Z., Maghooli, K., Soroush, P.Z., Tahvilian, P., Bagherzadeh, S.: EEG-based emotion recognition through nonlinear analysis. Int. J. Sci. Eng. Investig. 7(78), 62–69 (2018)

    Google Scholar 

  14. Alcaraz, R., Garca-Martnez, B., Zangrniz, R., Martnez-Rodrigo, A.: Recent advances and challenges in nonlinear characterization of brain dynamics for automatic recognition of emotional states. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, vol. 19, pp. 213–222. Springer, Cham (2017)

  15. Garca-Martnez, B., Martnez-Rodrigo, A., Alcaraz, R., Fernndez-Caballero, A., Gonzlez, P.: Nonlinear methodologies applied to automatic recognition of emotions: an EEG review. In: International Conference on Ubiquitous Computing and Ambient Intelligence, pp. 754-765. Springer, Cham (2017)

  16. Koelstra, S., Muehl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Article  Google Scholar 

  17. Kennel, M.B., Brown, R., Abarbanel, H.D.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45(6), 3403 (1992)

    Article  Google Scholar 

  18. Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33(2), 1134 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  19. Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401–408 (2012)

    Article  Google Scholar 

  20. Lashkari, S., Sheikhani, A., Golpayegan, M.R.H., Moghimi, A., Kobravi, H.R.: Topological feature extraction of nonlinear signals and trajectories and its application in EEG signals classification. Turk. J. Electr. Eng. Comput. Sci. 26, 1329–1342 (2018)

    Google Scholar 

  21. Sharif, B., Jafari, A.H.: Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincare plane. Comput. Methods Programs Biomed. 145, 11–22 (2017)

    Article  Google Scholar 

  22. Jarque, C.M., Bera, A.K.: Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ. Lett. 6(3), 255–259 (1980)

    Article  MathSciNet  Google Scholar 

  23. Hoseingholizade, S., Hashemi Golpaygani, M.R., Saburruh Monfared, A.: Studying emotion through nonlinear processing of EEG. Procedia-Soc. Behav. Sci. 32, 163–169 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Morteza Zangeneh Soroush & Keivan Maghooli

  2. Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Seyed Kamaledin Setarehdan

  3. Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

    Ali Motie Nasrabadi

Authors
  1. Morteza Zangeneh Soroush
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  2. Keivan Maghooli
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  3. Seyed Kamaledin Setarehdan
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  4. Ali Motie Nasrabadi
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Correspondence to Keivan Maghooli.

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Zangeneh Soroush, M., Maghooli, K., Setarehdan, S.K. et al. A novel EEG-based approach to classify emotions through phase space dynamics. SIViP 13, 1149–1156 (2019). https://doi.org/10.1007/s11760-019-01455-y

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  • DOI: https://doi.org/10.1007/s11760-019-01455-y

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