Neural Processing Letters

, Volume 45, Issue 3, pp 807–824 | Cite as

Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy

  • Md. Hedayetul Islam ShovonEmail author
  • Nanda Nandagopal
  • Ramasamy Vijayalakshmi
  • Jia Tina Du
  • Bernadine Cocks


Most previous studies of functional brain networks have been conducted on undirected networks despite the fact that direction of information flow is able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy (NTE) to detect and identify the patterns of information flow in the functional brain networks derived from EEG data during cognitive activity. Using a combination of signal processing, information and graph-theoretic techniques, this study has identified and characterized the changing connectivity patterns of the directed functional brain networks during different cognitive tasks. The functional brain networks constructed from EEG data using non-linear measure NTE also exhibit small-world property. An exponential truncated power-law fits the in-degree and out-degree distribution of directed functional brain networks. The empirical results demonstrate not only the application of transfer entropy in evaluating the directed functional brain networks, but also in determining the information flow patterns and thus provide more insights into the dynamics of the neuronal clusters underpinning cognitive function.


Transfer entropy Information flow Directed functional brain networks EEG Cognitive load Graph theory 



The authors wish to acknowledge the partial support provided by the Defence Science and Technology (DST) Group, Australia.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Md. Hedayetul Islam Shovon
    • 1
    Email author
  • Nanda Nandagopal
    • 1
  • Ramasamy Vijayalakshmi
    • 2
  • Jia Tina Du
    • 1
  • Bernadine Cocks
    • 1
  1. 1.Cognitive Neuroengineering and Computational Neuroscience Laboratory, School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia
  2. 2.Computational Neuroscience Laboratory, Department of Applied Mathematics and Computational SciencesPSG College of TechnologyCoimbatoreIndia

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