Skip to main content
Springer Nature Link
Log in

Comparison of Complexity and Regularity of ERP Recordings Between Single and Dual Tasks Using Sample Entropy Algorithm

  • Conference paper
Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

Included in the following conference series:

Abstract

The purpose of this study is to investigate the application of sample entropy (SampEn) measures to electrophysiological studies of single and dual tasking performance. The complexity of short-duration (~s) epochs of EEG data were analysed using SampEn along with the surrogate technique. Individual tasks consisted of an auditory discrimination task and two motor tasks of varying difficulty. Dual task conditions were combinations of one auditory and one motor task. EEG entropies were significantly lower in dual tasks compared to that in the single tasks. The results of this study have demonstrated that entropy measurements can be a useful alternative and nonlinear approach to analyzing short duration EEG signals on a time scale of seconds.

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

Access this chapter

Log in via an institution

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. Pashler, H.: Dual-Task Interference in Simple Tasks: Data and Theory. Psychological Bulletin 116(2), 220–244 (1994)

    Article  Google Scholar 

  2. Schumacher, E.H., Seymour, T.L., Glass, J.M., Fencsik, D.E., Lauber, E.J., Kieras, D.E., Meyer, D.E.: Virtually perfect time sharing in dual-task performance: Uncorking the central cognitive bottleneck. Psychological Science 12(2), 101–108 (2001)

    Article  Google Scholar 

  3. Hsieh, S., Yu, Y.-T.: Switching between simple response-set: inferences from the lateralized readiness potential. Cognitive Brain Research 17, 228–237 (2003)

    Article  Google Scholar 

  4. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J.D.: Testing for nonlinearity in time-series - the method of surrogate data. Physica D 58, 77–94 (1992)

    Article  MATH  Google Scholar 

  5. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. 278, H2039–H2049 (2000)

    Google Scholar 

  6. Monsell, S.: Task switching. Trends in Cognitive Science 7(3), 134–140 (2003)

    Article  Google Scholar 

  7. Pijn, J.P., van Neerven, J., Noest, A., Lopes da Silva, F.H.: Chaos or noise in EEG signals: dependence of state and brain side. Electroen. Clin. Neurophysiol. 79, 371–381 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Lab of Bioactive Materials of Ministry of Education and College of Life Science, Nankai University, Tianjin, 300071, PR China

    Tao Zhang & Xiaojun Tang

  2. College of Medicine Science, Nankai University, Tianjin, 300071, PR China

    Zhuo Yang

Authors
  1. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, T., Tang, X., Yang, Z. (2005). Comparison of Complexity and Regularity of ERP Recordings Between Single and Dual Tasks Using Sample Entropy Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_108

Download citation

  • DOI: https://doi.org/10.1007/11539087_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation