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Bayesian Graph Neural Networks for EEG-Based Emotion Recognition

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Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning (DCL 2021, PPML 2021, LL-COVID19 2021, CLIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12969))

Abstract

Emotion recognition has great significance in human-computer interaction, affective computing and clinical medicine, etc. Electroencephalography (EEG) is the most important one for emotion recognition due to its high temporal resolution. The progress in geometric deep learning provide powerful tool to explore the spatial features between EEG channels. There have been some studies using Graph-based methods, but neither do they reveal the latent structure of brain regions nor they contain uncertainty information. In this paper, we proposed a Bayesian Graph Neural Networks framework combined with a Sparse Graph Variational Auto-encoder. Our model can detect the latent communities between EEG channels in a non-parametric Bayesian way and provide uncertainty information of model prediction. Extensive experiments have been conducted to justify the effectiveness of our model and the results show that uncertainty information can help a lot.

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References

  1. Damasio, A.R.: Descartes’ Error: Emotion, Reason, and the Human Brain. Harper Perennial, New York (1995)

    Google Scholar 

  2. Khosla, A., Khandnor, P., Chand, T.: A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern. Biomed. Eng. 40, 649–690 (2020)

    Article  Google Scholar 

  3. Jenke, R., Peer, A., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3), 327–339 (2014)

    Article  Google Scholar 

  4. 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 

  5. Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015)

    Article  Google Scholar 

  6. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)

    Article  Google Scholar 

  7. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications. In: Proceedings of NIPS, pp. 1024–1034 (2017)

    Google Scholar 

  8. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of NIPS, pp. 3844–3852 (2016)

    Google Scholar 

  9. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of ICLR (2017)

    Google Scholar 

  10. Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS Workshop on Bayesian Deep Learning (2016)

    Google Scholar 

  11. Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532–541 (2020)

    Article  Google Scholar 

  12. Zhong, P.X., Wang, D., Miao, C.Y.: EEG-based emotion recognition using regularized graph neural networks. IEEE Transactions on Affective Computing (in press)

    Google Scholar 

  13. Ding, Y., Robinson, N., Zeng, Q.H., Guan, C.T.: LGGNet: learning from Local-global-graph representations for brain-computer interface. arXiv preprint arXiv:2105.02786 (2021)

  14. Wang, Z.M., Tong, Y., Heng, X.: Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access 7, 93711–93722 (2019)

    Article  Google Scholar 

  15. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of International Conference Machine Learning (2016)

    Google Scholar 

  16. Zhang, Y.X., Pal, S., Coates, M., Ustebay, D.: Bayesian graph convolutional neural networks for semi-supervised classification. In: Proceedings of Conference of AAAI (2019)

    Google Scholar 

  17. Hasanzadeh, A., Hajiramezanali, E., et al.: Bayesian graph neural networks with adaptive connection sampling. In: Proceedings of International Conference Machine Learning, PMLR, vol. 119 (2020)

    Google Scholar 

  18. Mehta, N., Duke, L.C., Rai, P.: Stochastic blockmodels meet graph neural networks. In: Proceedings International Conference Machine Learning, PMLR, vol. 97, pp. 4466–4474 (2019)

    Google Scholar 

  19. Achard, S., Bullmore, E.: Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3(2), e17 (2007)

    Article  Google Scholar 

  20. Mukhoti, J., Gal, Y.: Evaluating Bayesian deep learning methods for semantic segmentation. arXiv preprint arXiv:1811.12709 (2019)

  21. Gal, Y., Hron, J., Kendall, A.: Concrete dropout. arXiv preprint arXiv:1705.07832 (2017)

  22. Paszke, A., Gross, S., et al.: PyTorch: an imperative style, high-performance deep learning library. In Conference NeurIPS (2019)

    Google Scholar 

  23. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR (2019)

    Google Scholar 

  24. Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, Y.: Spatial-temporal recurrent neural network for emotion recognition. IEEE Trans. Cybern. 99, 1–9 (2018)

    Google Scholar 

  25. Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., Zhou, X.: A bi-hemisphere domain adversarial neural network model for EEG emotion recognition. IEEE Transactions on Affective Computing (2018, in press)

    Google Scholar 

  26. Li, Y., et al.: A novel bi-hemispheric discrepancy model for EEG emotion recognition. arXiv preprint arXiv:1906.01704 (2019)

  27. Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVM. J. Mach. Learn. Res. 7, 1687–1712 (2016)

    MathSciNet  MATH  Google Scholar 

  28. Li, H., Jin, Y.-M., Zheng, W.-L., Bao-Liang, L.: Cross-subject emotion recognition using deep adaptation networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11305, pp. 403–413. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04221-9_36

    Chapter  Google Scholar 

  29. Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)

  30. Griffiths, T.L., Ghahramani, Z.: The Indian buffet process: an introduction and review. J. Mach. Learn. Res. 12, 1185–1224 (2011)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Tongji University, Shanghai, 201804, China

    Jianhui Chen, Hui Qian & Xiaoliang Gong

Authors
  1. Jianhui Chen
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  2. Hui Qian
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  3. Xiaoliang Gong
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Corresponding author

Correspondence to Xiaoliang Gong .

Editor information

Editors and Affiliations

  1. Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura

  2. King's College London, London, UK

    M. Jorge Cardoso

  3. IBM Research-Haifa and Haifa University, Haifa, Israel

    Michal Rosen-Zvi

  4. Technical University of Munich, Munich, Germany

    Georgios Kaissis

  5. Children’s National Health System, Washington, D.C., DC, USA

    Marius George Linguraru

  6. Children’s National Health System, Washington, DC, USA

    Raj Shekhar

  7. Fraunhofer IGD, Darmstadt, Germany

    Stefan Wesarg

  8. Fraunhofer Singapore, Singapore, Singapore

    Marius Erdt

  9. Aachen University of Applied Sciences, Jülich, Germany

    Klaus Drechsler

  10. Tongji University, Shanghai, China

    Yufei Chen

  11. Helmholtz AI, Neuherberg, Germany

    Shadi Albarqouni

  12. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  13. Vanderbilt University, Nashville, TN, USA

    Bennett Landman

  14. NVIDIA GmbH, Munich, Germany

    Nicola Rieke

  15. NVIDIA Corporation, Bethesda, MD, USA

    Holger Roth

  16. University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  17. NVIDIA Corporation, Santa Clara, CA, USA

    Daguang Xu

  18. IBM Research Europe, Rueschlikon, Switzerland

    Maria Gabrani

  19. Computer Vision Laboratory, Zürich, Switzerland

    Ender Konukoglu

  20. Assuta Medical Centers Radiology, Aviv-Yafo, Israel

    Michal Guindy

  21. Technical University of Munich, Munich, Germany

    Daniel Rueckert

  22. Technical University of Munich, Munich, Germany

    Alexander Ziller

  23. Technical University of Munich, Munich, Germany

    Dmitrii Usynin

  24. Imperial College London, London, UK

    Jonathan Passerat-Palmbach

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Chen, J., Qian, H., Gong, X. (2021). Bayesian Graph Neural Networks for EEG-Based Emotion Recognition. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-90874-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90873-7

  • Online ISBN: 978-3-030-90874-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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