Skip to main content
Springer Nature Link
Log in

A New Emotion Classification Method Based on JAN-VMD

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14885))

  • 482 Accesses

Abstract

The research on emotion classification based on electroencephalogram(EEG) signal has been increasingly studied for its applicability in human-computer interaction. However, the effectiveness of EEG emotion classification is often suboptimal due to person, time, and equipment variations in EEG data, and this remains a major challenge in real-world applications. For the purpose of improving the identification of EEG emotion, this paper proposes a new method for classifying emotion, called Joint Adaptation Network-Variational Mode Decomposition (JAN-VMD), which combines the universality and effectiveness of transfer learning in the field of EEG emotion classification. First, we optimize the differential mode decomposition based on multiple group differences. Then, we apply the optimized differential mode decomposition combined with transfer learning to various scenarios of EEG-based emotion classification experiments. Finally, experimental results confirmed that the proposed JAN-VMD model in this paper effectively improves the accuracy of EEG-based emotion classification. The experiments demonstrated that the proposed method not only effectively addresses the issue of differential recognition in deep neural networks but also enhances recognition accuracy on the basis of transfer learning algorithms, proving the feasibility and superiority of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://bcmi.sjtu.edu.cn/home/seed/seed.html.

  2. 2.

    https://bcmi.sjtu.edu.cn/home/seed/seed-iv.html.

  3. 3.

    https://www.eecs.qmul.ac.uk/mmv/datasets/deap/.

References

  1. Canal, F.Z., et al.: A survey on facial emotion recognition techniques: a state-of-the-art literature review. Inf. Sci. 582, 593–617 (2022)

    Article  Google Scholar 

  2. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Article  Google Scholar 

  3. Doma, V., Pirouz, M.: A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. J. Big Data 7(1), 1–21 (2020)

    Article  Google Scholar 

  4. Forgas, J.P.: Mood effects on cognition: affective influences on the content and process of information processing and behavior. In: Emotions and Affect in Human Factors and Human-Computer Interaction, pp. 89–122 (2017)

    Google Scholar 

  5. Ko, W., Jeon, E., Jeong, S., Suk, H.I.: Multi-scale neural network for EEG representation learning in BCI. IEEE Comput. Intell. Mag. 16(2), 31–45 (2021)

    Article  Google Scholar 

  6. Lan, Z., Sourina, O., Wang, L., Scherer, R., Müller-Putz, G.R.: Domain adaptation techniques for EEG-based emotion recognition: a comparative study on two public datasets. IEEE Trans. Cogn. Dev. Syst. 11(1), 85–94 (2018)

    Article  Google Scholar 

  7. Li, J., et al.: Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning. Comput. Biol. Med. 145, 105519 (2022)

    Article  Google Scholar 

  8. Li, Y., Zheng, W., Wang, L., Zong, Y., Cui, Z.: From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition. IEEE Trans. Affect. Comput. 13(2), 568–578 (2019)

    Article  Google Scholar 

  9. Liu, H., Guo, H., Hu, W.: EEG-based emotion classification using joint adaptation networks. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2021)

    Google Scholar 

  10. Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105. PMLR (2015)

    Google Scholar 

  11. Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208–2217. PMLR (2017)

    Google Scholar 

  12. Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2010)

    Article  Google Scholar 

  13. Singh, Y.B., Goel, S.: A systematic literature review of speech emotion recognition approaches. Neurocomputing 492, 245–263 (2022)

    Article  Google Scholar 

  14. 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 (2018)

    Article  Google Scholar 

  15. Wang, Y., et al.: A systematic review on affective computing: emotion models, databases, and recent advances. Information Fusion 83, 19–52 (2022)

    Article  Google Scholar 

  16. Yang, F., Zhao, X., Jiang, W., Gao, P., Liu, G.: Multi-method fusion of cross-subject emotion recognition based on high-dimensional eeg features. Front. Comput. Neurosci. 13, 53 (2019)

    Article  Google Scholar 

  17. Zhu, Z., Lin, K., Jain, A.K., Zhou, J.: Transfer learning in deep reinforcement learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 13344–13362 (2023)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science, Wuhan University of Science and Technology, Wuhan, China

    Qiming Zhao, Jing Wu & Hong Liu

  2. Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems, Wuhan, China

    Qiming Zhao, Jing Wu & Hong Liu

Authors
  1. Qiming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Wu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Cungeng Cao

  2. Zhejiang University, Zhejiang, China

    Huajun Chen

  3. Emory University, Atlanta, GA, USA

    Liang Zhao

  4. Birmingham City University, Birmingham, UK

    Junaid Arshad

  5. Monash University, Banten, Indonesia

    Taufiq Asyhari

  6. Birmingham City University, Birmingham, UK

    Yonghao Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, Q., Wu, J., Liu, H. (2024). A New Emotion Classification Method Based on JAN-VMD. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14885. Springer, Singapore. https://doi.org/10.1007/978-981-97-5495-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-5495-3_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5494-6

  • Online ISBN: 978-981-97-5495-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics