Skip to main content
SpringerLink
Log in

An EEG Emotion Classification System Based on One-Dimension Convolutional Neural Networks and Virtual Reality

  • Conference paper
  • First Online:
Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1195))

  • 1285 Accesses

Abstract

The increase in the number of patients with Alzheimer’s disease has placed a heavy burden on society and has become a major problem in the medical field. In recent years, brain-computer interface (BCI) has become an important way to explore the utilization of modern technology to improve Alzheimer’s disease. However, traditional pattern recognition methods suffer from poor classification and feature extraction. This paper proposes an end-to-end Electroencephalograph (EEG) emotion classification method based on 1D-CNN (one-dimension convolutional neural networks) to improve the accuracy of BCI pattern recognition. An application system combining virtual reality (VR) and BCI is further constructed, which could help patients to repeatedly perform memory stimulation and provide a new clue for clinical treatment of Alzheimer’s disease.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Bhatkoti, P., Paul, M.: Early diagnosis of Alzheimer’s disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE (2016)

    Google Scholar 

  2. Jessen, F., et al.: A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s Dement. 10(6), 844–852 (2014)

    Article  Google Scholar 

  3. Brookmeyer, R., et al.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. 3(3), 186–191 (2007)

    Article  Google Scholar 

  4. Cummings, J.L., Morstorf, T., Zhong, K.: Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res. Ther. 6(4), 37 (2014)

    Article  Google Scholar 

  5. Cohene, T., et al.: Memories of a life: a design case study for Alzheimer’s disease, pp. 357–387. Universal Usability, John Wiley & Sons (2007)

    Google Scholar 

  6. Baek, H.J., et al.: Ergonomic issues in brain-computer interface technologies: current status, challenges, and future direction. Computational Intelligence and Neuroscience 2020 (2020)

    Google Scholar 

  7. Waldrop, M.M: News feature: virtual reality therapy set for a real renaissance. Proc. Nat. Acad. Sci. 114(39), 10295–10299 (2017)

    Article  Google Scholar 

  8. Bamdad, M., Zarshenas, H., Auais, M.A.: Application of BCI systems in neurorehabilitation: a scoping review. Disab. Rehabil. Assist. Technol. 10(5), 355–364 (2015)

    Article  Google Scholar 

  9. Joadder, Md.A.M., et al.: A new design of mental state classification for subject independent BCI systems. IRBM 40(5), 297–305 (2019)

    Google Scholar 

  10. Goodfellow, I., et al.: Deep Learning, vol. 1. Cambridge (2016)

    Google Scholar 

  11. Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)

    Article  Google Scholar 

  12. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  13. Stober, S., et al.: Deep feature learning for EEG recordings. arXiv preprint arXiv:1511.04306 (2015)

  14. Yang, Y., et al.: Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE (2018)

    Google Scholar 

  15. Bao, X., et al.: Mechanism of Kinect-based virtual reality training for motor functional recovery of upper limbs after subacute stroke. Neural Regener. Res. 8(31), 2904 (2013)

    Google Scholar 

  16. Wang, G.-X., Li, L.: Virtual reality exposure therapy of anxiety disorders. Adv. Psychol. Sci. 20(8), 1277–1286 (2012)

    Article  Google Scholar 

  17. Amores, J., et al.: Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE (2018)

    Google Scholar 

  18. Zhang, S., et al.: Graph theoretical analysis of EEG functional network during multi-workload flight simulation experiment in virtual reality environment. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2017)

    Google Scholar 

  19. Wang, X.-W., Nie, D., Bao-Liang, L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)

    Article  Google Scholar 

  20. Botvinick, M.M., et al.: Conflict monitoring and cognitive control. Psychol. Rev. 108(3), 624 (2001)

    Article  Google Scholar 

  21. Riccitiello, J.: John Riccitiello sets out to identify the engine of growth for Unity Technologies (interview). VentureBeat. Interview with Dean Takahashi, vol. 3 (2015). Accessed 18 Jan 2015

    Google Scholar 

Download references

Acknowledgments

Supported by the Fundamental Research Funds for the Central Universities under Grant Number: N2017003 and N2017004.

Author information

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110004, China

    Xinbei Jiang & Tianhan Gao

Authors
  1. Xinbei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianhan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianhan Gao .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Institute of Information Technology, Lodz University of Technology, Łódź, Poland

    Aneta Poniszewska-Maranda

  3. Department of Information and Communications Engineering, Myongji University, Yongin, Seoul, Korea (Republic of)

    Hyunhee Park

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, X., Gao, T. (2021). An EEG Emotion Classification System Based on One-Dimension Convolutional Neural Networks and Virtual Reality. In: Barolli, L., Poniszewska-Maranda, A., Park, H. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2020. Advances in Intelligent Systems and Computing, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-030-50399-4_19

Download citation

Publish with us

Policies and ethics

Search

Navigation