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Emotion Recognition Using Multi-core Tensor Learning and Multimodal Physiological Signal

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Human Brain and Artificial Intelligence (HBAI 2021)

Abstract

Emotion recognition based on multimodal physiological signal has attracted a bunch of attention. Tensor learning helps to extract effective shared features from multi-modal high-dimensional data. However, in tensor decomposition, the determination of the core size has always been a difficult problem, resulting in the loss of effective feature information. In this paper, we propose a multi-core voting tensor learning method, namely MCVTL, for multimodal emotion analysis, which try to improve the results of emotion recognition by fusing multi-core information of various scales. Especially, through fusing the knowledge with 4 cores, the performance is improved by nearly 5% in valence and 6% in arousal compared with the single-core case. The empirical results demonstrate the effectiveness of the proposed method.

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References

  1. Abtahi, F., Ro, T., Li, W., Zhu, Z.: Emotion analysis using audio/video, EMG and EEG: a dataset and comparison study. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 10–19. IEEE (2018)

    Google Scholar 

  2. Candra, H., et al.: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7250–7253. IEEE (2015)

    Google Scholar 

  3. Chao, H., Dong, L., Liu, Y., Lu, B.: Emotion recognition from multiband EEG signals using CapsNet. Sensors 19(9), 2212 (2019)

    Article  Google Scholar 

  4. Cong, F., Lin, Q.H., Kuang, L.D., Gong, X.F., Astikainen, P., Ristaniemi, T.: Tensor decomposition of EEG signals: a brief review. J. Neurosci. Methods 248, 59–69 (2015)

    Article  Google Scholar 

  5. Daimi, S.N., Saha, G.: Classification of emotions induced by music videos and correlation with participants’ rating. Expert Syst. Appl. 41(13), 6057–6065 (2014)

    Article  Google Scholar 

  6. De Lathauwer, L., De Moor, B., Vandewalle, J.: On the best rank-1 and rank-(R\(_{1}\), R\(_{2}\),..., R\(_N\)) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324–1342 (2000)

    Google Scholar 

  7. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 2, pp. 985–990. IEEE (2004)

    Google Scholar 

  8. Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2011)

    Article  Google Scholar 

  9. Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H.: EEG-based emotion recognition in music listening: a comparison of schemes for multiclass support vector machine. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 489–492. IEEE (2009)

    Google Scholar 

  10. Mert, A., Akan, A.: Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern Anal. Appl. 21(1), 81–89 (2018). https://doi.org/10.1007/s10044-016-0567-6

    Article  MathSciNet  Google Scholar 

  11. Mill, A., Allik, J., Realo, A., Valk, R.: Age-related differences in emotion recognition ability: a cross-sectional study. Emotion 9(5), 619 (2009)

    Article  Google Scholar 

  12. Mohammadi, Z., Frounchi, J., Amiri, M.: Wavelet-based emotion recognition system using EEG signal. Neural Comput. Appl. 28(8), 1985–1990 (2017). https://doi.org/10.1007/s00521-015-2149-8

    Article  Google Scholar 

  13. Nikolova, D., Mihaylova, P., Manolova, A., Georgieva, P.: ECG-based human emotion recognition across multiple subjects. In: Poulkov, V. (ed.) FABULOUS 2019. LNICST, vol. 283, pp. 25–36. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23976-3_3

    Chapter  Google Scholar 

  14. Phan, A.H., Cichocki, A.: Tensor decompositions for feature extraction and classification of high dimensional datasets. Nonlinear Theory Appl. IEICE 1(1), 37–68 (2010)

    Article  Google Scholar 

  15. Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715 (2005)

    Article  Google Scholar 

  16. Verma, G.K., Tiwary, U.S.: Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage 102, 162–172 (2014)

    Article  Google Scholar 

  17. Vogt, T., André, E.: Improving automatic emotion recognition from speech via gender differentiation. In: LREC, pp. 1123–1126 (2006)

    Google Scholar 

  18. Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. 10, 417–429 (2017)

    Article  Google Scholar 

  19. Zhou, G., Cichocki, A., Xie, S.: Fast nonnegative matrix/tensor factorization based on low-rank approximation. IEEE Trans. Signal Process. 60(6), 2928–2940 (2012)

    Article  MathSciNet  Google Scholar 

  20. Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H., Yan, B.: Emotion recognition from EEG signals using multidimensional information in EMD domain. BioMed Res. Int. 2017, 1–9 (2017)

    Article  Google Scholar 

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Acknowledgments

This work was supported by NSFC (61633010, 61671193, 61602140), National Key Research & Development Project (2017YFE0116800), Key Research & Development Project of Zhejiang Province (2020C04009, 2018C04012).

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Correspondence to Jianhai Zhang .

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Xu, H., Tang, J., Zhang, J., Zhu, L. (2021). Emotion Recognition Using Multi-core Tensor Learning and Multimodal Physiological Signal. In: Wang, Y. (eds) Human Brain and Artificial Intelligence. HBAI 2021. Communications in Computer and Information Science, vol 1369. Springer, Singapore. https://doi.org/10.1007/978-981-16-1288-6_10

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  • DOI: https://doi.org/10.1007/978-981-16-1288-6_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1287-9

  • Online ISBN: 978-981-16-1288-6

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