[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Classification of EEG Signals Under Different Brain Functional States Using RBF Neural Network

  • Conference paper
Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

Included in the following conference series:

Abstract

Investigation of the states of human brain through the elec-troencephalograph (EEG) is an important application of EEG signals. This paper describes the application of an artificial neural network technique together with a feature extraction technique, the wavelet packet transformation, in classification of EEG signals. Feature vector is extracted by wavelet packet transform. Artificial neural network is used to recognize the brain statues. After training, the BP and RBF neural network are able to correctly classify the brain states, respectively. This method is potentially powerful for brain states classification.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Sumathy, S., Krishnan, C.N.: Automatic Machine Classification of Patient Anaesthesia Levels Using EEG Signals. In: IECON Proceedings, vol. 3, pp. 2349–2351. IEEE, Los Alamitos (1991)

    Google Scholar 

  2. Shimada, T., Shiina, T., Saito, Y.: Detection of Characteristic Waves of Sleep EEG by Neural Network Analysis. In: IEEE Transactions on Biomedical Engineering, vol. 47, pp. 369–379. IEEE, Piscataway (2000)

    Google Scholar 

  3. Rosso, O.A., et al.: Brain Electrical Activity Analysis Using Wavelet-based Informational Tools. Physica A 313, 587–608 (2002)

    Article  MATH  Google Scholar 

  4. Rosso, O.A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., SchWurmann, M., BaHsar, E.: Wavelet Entropy: a New Tool for Analysis of Short Duration Brain Electrical Signals. Journal of Neuroscience Methods 105, 65–75 (2001)

    Article  Google Scholar 

  5. Hazarika, Neep, Chen, Jean Zhu, Tsoi, Ah Chung, Sergejew, Alex: Classification of EEG Signals using the Wavelet Transform. Signal Processing, Vol. 59. Elsevier, Amsterdam (1997) 61-72 7. Tong, S., Bezerianos, A Paul J., Zhu Y. Thakor, N: Nonextensive Entropy Measure of EEG Following Brain Injury from Cardiac Arrest. Physica A: Statistical Mechanics and its Applications, Vol. 305. (2002) 619-628 8. 9. Hou Xinguo, Xie Li, Wu Zhengguo, Zhao Yong-ling: Fault Diagnosis Method for Induction Motor Based on Wavelet Transformation and Neural Network, Journal of Data Acquisition & Processing. Vol.19 (2004) 32-36

    Article  MATH  Google Scholar 

  6. Tong, S., Bezerianos, A., Paul, J., Zhu, Y., Thakor, N.: Nonextensive Entropy Measure of EEG Following Brain Injury from Cardiac Arrest. Physica A: Statistical Mechanics and its Applications 305, 619–628 (2002)

    Article  MATH  Google Scholar 

  7. Ali, Y., Ke, Y., et al.: Application of Wavelet Neural Network in EEG Signals Data Compression Representation and Spikes Recognition. Chinese Journal of Biomedical Engineering 18, 142–148 (1999)

    Google Scholar 

  8. Xinguo, H., Li, X., Zhengguo, W., Yong-ling, Z.: Fault Diagnosis Method for Induction Motor Based on Wavelet Transformation and Neural Network. Journal of Data Acquisition & Processing 19, 32–36 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, Z., Shen, M., Beadle, P. (2004). Classification of EEG Signals Under Different Brain Functional States Using RBF Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-28648-6_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics