Skip to main content
SpringerLink
Log in

A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data

  • Conference paper
  • First Online:
Medical Image Learning with Limited and Noisy Data (MILLanD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13559))

Included in the following conference series:

Abstract

In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features’ Gram matrices into attention matrices for better feature refinement. Our experiments have shown that MCAM’s effectiveness is comparable to state-of-the-art attention modules in boosting the backbone network’s performance in prediction while requiring less parameters. Several accompanying sensitivity analyses on trained models’ prediction concerning different attacks are also performed. These attacks include various frequency domain filtering levels and gradually morphing between samples associated with multiple labels. Our results can help better understand different modules’ behaviour in prediction and can provide guidance in applications where data is limited and are with noises.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Acharya, D., Ahmed Sayyad, R., Dwivedi, P., Shaji, A., Sriram, P., Bhardwaj, A.: EEG signal classification using deep learning. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds.) Soft Computing for Problem Solving. AISC, vol. 1392, pp. 393–403. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-2709-5_30

  2. Bang, J.W., Choi, J.S., Park, K.R.: Noise reduction in brainwaves by using both EEG signals and frontal viewing camera images. Sensors (Basel, Switzerland) 13, 6272–6294 (2013)

    Article  Google Scholar 

  3. Blankertz, B., Kawanabe, M., Tomioka, R., Hohlefeld, F., Müller, K.r., Nikulin, V.V.: Invariant common spatial patterns: alleviating nonstationarities in brain-computer interfacing. In: Advances in Neural Information Processing Systems, pp. 113–120 (2008)

    Google Scholar 

  4. Cao, J., Li, J., Hu, X., Wu, X., Tan, M.: Towards interpreting deep neural networks via layer behavior understanding. Mach. Learn. 111, 1159–1179 (2022)

    Google Scholar 

  5. Cecotti, H., Graser, A.: Convolutional neural networks for p300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 433–445 (2010)

    Article  Google Scholar 

  6. Congedo, M., Barachant, A., Bhatia, R.: Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput. Interf. 4(3), 155–174 (2017)

    Article  Google Scholar 

  7. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  8. Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K.R., Grozea, C.: Subject-independent mental state classification in single trials. Neural Netw. 22(9), 1305–1312 (2009)

    Article  Google Scholar 

  9. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)

    Google Scholar 

  10. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  11. Khateeb, M., Anwar, S.M., Alnowami, M.R.: Multi-domain feature fusion for emotion classification using DEAP dataset. IEEE Access 9, 12134–12142 (2021)

    Article  Google Scholar 

  12. Koelstra, S., Muhl, 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). https://doi.org/10.1109/T-AFFC.2011.15

    Article  Google Scholar 

  13. Koizumi, Y., Yatabe, K., Delcroix, M., Masuyama, Y., Takeuchi, D.: Speech enhancement using self-adaptation and multi-head self-attention. ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 181–185 (2020)

    Google Scholar 

  14. Kulkarni, S., Patil, P.R.: Analysis of DEAP dataset for emotion recognition. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds.) International Conference on Intelligent and Smart Computing in Data Analytics. AISC, vol. 1312, pp. 67–76. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6176-8_8

    Chapter  Google Scholar 

  15. Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional network for EEG-based brain-computer interfaces. CoRR abs/1611.08024 (2016), http://arxiv.org/abs/1611.08024

  16. Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. ArXiv abs/1904.08082 (2019)

    Google Scholar 

  17. Leite, N.M.N., Pereira, E.T., Gurjao, E.C., Veloso, L.R.: Deep convolutional autoencoder for EEG noise filtering. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2605–2612. IEEE (2018)

    Google Scholar 

  18. Li, J., Zhang, L.: Bilateral adaptation and neurofeedback for brain computer interface system. J. Neurosci. Methods 193(2), 373–379 (2010)

    Article  MathSciNet  Google Scholar 

  19. Liu, G., Huang, G., Meng, J., Zhang, D., Zhu, X.: Improved GMM with parameter initialization for unsupervised adaptation of brain-computer interface. Int. J. Num. Methods Biomed. Eng. 26(6), 681–691 (2010)

    MATH  Google Scholar 

  20. Liu, G., Zhang, D., Meng, J., Huang, G., Zhu, X.: Unsupervised adaptation of electroencephalogram signal processing based on fuzzy c-means algorithm. Int. J. Adapt. Control Sig. Process. 26(6), 482–495 (2012)

    Article  MathSciNet  Google Scholar 

  21. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15(3) (2018)

    Google Scholar 

  22. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R (2007)

    Google Scholar 

  23. Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans. Neural Syst. Rehab. Eng. 25(6), 566–576 (2016)

    Google Scholar 

  24. Mittag, G., Naderi, B., Chehadi, A., Möller, S.: Nisqa: A deep CNN-self-attention model for multidimensional speech quality prediction with crowdsourced datasets. In: Interspeech (2021)

    Google Scholar 

  25. Repov, G.: Dealing with noise in EEG recording and data analysis. Inform. Medica Slovenica 15 (2010)

    Google Scholar 

  26. Thejaswini, Ravi Kumar, K.M., Aditya Nataraj, J.L.: Analysis of EEG based emotion detection of DEAP and SEED-IV databases using SVM. SSRN Electr. J. 8 (2019)

    Google Scholar 

  27. Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H.: DySAT: deep neural representation learning on dynamic graphs via self-attention networks. Proceedings of the 13th International Conference on Web Search and Data Mining (2020)

    Google Scholar 

  28. Schlögl, A., Vidaurre, C., Müller, K.R.: Adaptive methods in BCI research - an introductory tutorial. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces. The Frontiers Collection, pp. 331–355. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02091-9_18

  29. Sreeram, V., Agathoklis, P.: On the properties of gram matrix. IEEE Trans. Circ. Syst. I Fundam. Theory Appl. 41(3), 234–237 (1994)

    Google Scholar 

  30. Stajić, T., Jovanović, J., Jovanović, N., Jankovic, M.M.: Emotion recognition based on DEAP database physiological signals. In: 2021 29th Telecommunications Forum (TELFOR), pp. 1–4 (2021)

    Google Scholar 

  31. Steyrl, D., Scherer, R., Faller, J., Müller-Putz, G.R.: Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomed. Eng./Biomedizinische Technik 61(1), 77–86 (2016)

    Google Scholar 

  32. Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017). http://arxiv.org/abs/1706.03762

  33. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp. 3–19 (2018)

    Google Scholar 

Download references

Acknowledgement

This work was partially supported by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (22qntd2901).

Author information

Authors and Affiliations

  1. School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai, Guangdong, China

    Dongyang Kuang, Wenting Li & Rui Guo

  2. Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, USA

    Craig Michoski

Authors
  1. Dongyang Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Craig Michoski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenting Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongyang Kuang .

Editor information

Editors and Affiliations

  1. National Institutes of Health, Bethesda, MD, USA

    Ghada Zamzmi

  2. National Institutes of Health, Bethesda, MD, USA

    Sameer Antani

  3. Northwestern University, Chicago, IL, USA

    Ulas Bagci

  4. Children's National Hospital, Washington, WA, USA

    Marius George Linguraru

  5. National Institutes of Health, Bethesda, MD, USA

    Sivaramakrishnan Rajaraman

  6. National Institutes of Health, Bethesda, MD, USA

    Zhiyun Xue

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kuang, D., Michoski, C., Li, W., Guo, R. (2022). A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16760-7_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16759-1

  • Online ISBN: 978-3-031-16760-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation