Skip to main content

Sonification for EEG Frequency Spectrum and EEG-Based Emotion Features

  • Conference paper
  • 4456 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

Abstract

Sonification is the use of representations of data through sound to convey information. It is particularly meaningful if the data are involved in time. This paper present a hybrid sonification method and aims to directly expressed the emotion hidden in the EEG signal through sound. The hybrid method mainly consists of two parts: (1) Frequency Mapping Representation (FMR) and (2) Emotion Feature Representation (EFR).

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

Buying options

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhang, H., Liang, J., Liu, Y., Wang, H., Zhang, L.: An iterative method for classifying stroke subjects motor imagery eeg data in the bci-fes rehabilitation training system. In: Sun, F., Hu, D., Liu, H. (eds.) Foundations and Practical Applications of Cognitive Systems and Information Processing. AISC, vol. 215, pp. 363–373. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  2. Duan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for eeg-based emotion classification. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 81–84. IEEE (2013)

    Google Scholar 

  3. Gaver, W.W.: Using and creating auditory icons (1994)

    Google Scholar 

  4. Kramer, G.: Auditory display: Sonification, audification, and auditory interfaces. Addison-Wesley, Reading (1994)

    Google Scholar 

  5. Scaletti, C.: Sound synthesis algorithms for auditory data representations. In: Santa Fe Institude Studies in the Sciences of Complexity-Proceedings, vol. 18, p. 223. Addison-Wesley Publishing Co. (1994)

    Google Scholar 

  6. Hermann, T., Ritter, H.: Listen to your data: Model-based sonification for data analysis. Advances in Intelligent Computing and Multimedia Systems 8, 189–194 (1999)

    Google Scholar 

  7. Baier, G., Hermann, T., Stephani, U.: Multi-channel sonification of human eeg. In: Proceedings of the 13th International Conference on Auditory Display (2007)

    Google Scholar 

  8. Wu, D., Li, C.Y., Yao, D.Z.: Scale-free music of the brain. PloS One 4(6), e5915 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, Y., Huang, Y., Yue, J., Zhang, L. (2014). Sonification for EEG Frequency Spectrum and EEG-Based Emotion Features. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12643-2_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics