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EEG-Based Emotion Recognition – Evaluation Methodology Revisited

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Computational Science – ICCS 2021 (ICCS 2021)

Abstract

The challenge of EEG-based emotion recognition had inspired researchers for years. However, lack of efficient technologies and methods of EEG signal analysis hindered the development of successful solutions in this domain. Recent advancements in deep convolutional neural networks (CNN), facilitating automatic signal feature extraction and classification, brought a hope for more efficient problem solving. Unfortunately, vague and subjective interpretation of emotional states limits effective training of deep models, especially when binary classification is performed basing on datasets with non-bimodal distribution of emotional state ratings. In this work we revisited the methodology of emotion recognition, proposing to use regression instead of classification, along with appropriate result evaluation measures based on mean absolute error (MAE) and mean squared error (MSE). The advantages of the proposed approach are clearly demonstrated on the example of the well-established and explored DEAP dataset.

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References

  1. Opałka, S., Stasiak, B., Szajerman, D., Wojciechowski, A.: Multi-channel convolutional neural networks architecture feeding for effective EEG mental tasks classification. Sensors 18, 3451 (2018)

    Google Scholar 

  2. Teplan, M.: Fundamentals of EEG measurement. Meas. Sci. 2, 1–11 (2002)

    Google Scholar 

  3. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  4. Lazarek, J., Pryczek, M.: A review of point cloud semantic segmentation methods. J. Appl. Comput. Sci. 26(2), 99–105 (2018)

    Google Scholar 

  5. Li, G., Lee, C.H., Jung, J.J., Youn, Y.C., Camacho, D., Deep learning for EEG data analytics: a survey. Concurrency Comput. 32(18), e5199 (2020)

    Google Scholar 

  6. Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3, 18–31 (2012). https://doi.org/10.1109/T-AFFC.2011.15

    Article  Google Scholar 

  7. Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42–55 (2012). https://doi.org/10.1109/T-AFFC.2011.25

  8. Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)

    Google Scholar 

  9. Craik, A., He, Y., Contreras-Vidal, J.L., Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Eng. 16(3), 031001 (2019)

    Google Scholar 

  10. Vrbancic, G., Podgorelec, V.: Automatic classification of motor impairment neural disorders from EEG signals sing deep convolutional neural networks. Elektron. Elektrotech. 24, 3–7 (2018)

    Article  Google Scholar 

  11. Kuanar, S., Athitsos, V., Pradhan, N., Mishra, A., Rao, K.R.: Cognitive analysis of working memory load from EEG, by a deep recurrent neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2018)

    Google Scholar 

  12. Vilamala, A., Madsen, K.H., Hansen, L.K.: Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) (2017)

    Google Scholar 

  13. Jiao, Z., Gao, X., Wang, Y., Li, J., Xu, H.: Deep Convolutional Neural Networks for mental load classification based on EEG data. Pattern Recog. 76, 582–95 (2018)

    Article  Google Scholar 

  14. Li, X., Song, D., Zhang, P., Yu G., Hou, Y., Hu, B.: Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine, pp. 352–359 (2016)

    Google Scholar 

  15. Li, Y., Huang, J., Zhou, H., Zhong, N.: Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks. Appl. Sci. 7, 1060 (2017)

    Article  Google Scholar 

  16. Yanagimoto, M., Sugimoto, C.: Recognition of persisting emotional valence from EEG using convolutional neural networks. In: 2016 IEEE 9th International Workshop on Computational Intelligence and Applications, pp. 27–32 (2016)

    Google Scholar 

  17. Qiao, R., Qing, C., Zhang, T., Xing, X., Xu, X.: A novel deep-learning based framework for multi-subject emotion recognition. In: 2017 4th International Conference on Information, Cybernetics and Computational Social SystemsICCSS, pp. 181–185 (2017)

    Google Scholar 

  18. Salama, E.S., El-khoribi, R.A., Shoman, M.E., Shalaby, M.A.: EEG-based emotion recognition using 3D convolutional neural networks. Int. J. Adv. Comput. Sci. Appl. 9, 329–37 (2018)

    Google Scholar 

  19. Lin, W., Li, C., Sun, S.: Deep convolutional neural network for emotion recognition using EEG and peripheral physiological signal. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 385–394. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_33

    Chapter  Google Scholar 

  20. Nakisa, B., Rastgoo, M.N., Tjondronegoro, D., Chandran, V.: Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst. Appl. 93, 143–155 (2018)

    Article  Google Scholar 

  21. Asghar, M.A., et al.: EEG-based multi-modal emotion recognition using bag of deep features: an optimal feature selection approach. Sensors 19(23), 5218 (2019)

    Google Scholar 

  22. Yin, Z., Wang, Y., Liu, L., Zhang, W., Zhang, J. Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination. Front. Neurorobotics 11, 19 (2017)

    Google Scholar 

  23. Menezes, M.L.R., et al.: Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset. Pers. Ubiq. Comp. 1–11 (2017)

    Google Scholar 

  24. 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 

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Correspondence to Bartłomiej Stasiak .

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Opałka, S., Stasiak, B., Wosiak, A., Dura, A., Wojciechowski, A. (2021). EEG-Based Emotion Recognition – Evaluation Methodology Revisited. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_40

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  • DOI: https://doi.org/10.1007/978-3-030-77964-1_40

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