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Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity

  • Md. Hedayetul Islam Shovon
  • D (Nanda) Nandagopal
  • Ramasamy Vijayalakshmi
  • Jia Tina Du
  • Bernadine Cocks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8834)

Abstract

Most previous studies of functional brain networks have been conducted on undirected networks despite the direction of information flow able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy to EEG data to detect and identify the patterns of information flow in the functional brain networks during cognitive activity. Using a mix 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 results demonstrate not only the value of transfer entropy in evaluating the directed functional brain networks but more importantly in determining the information flow patterns and thus providing more insights into the dynamics of the neuronal clusters underpinning cognitive function.

Keywords

Transfer entropy directed functional brain network EEG cognitive load graph theory 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Md. Hedayetul Islam Shovon
    • 1
  • D (Nanda) Nandagopal
    • 1
  • Ramasamy Vijayalakshmi
    • 2
  • Jia Tina Du
    • 1
  • Bernadine Cocks
    • 1
  1. 1.Cognitive Neuroengineering Laboratory, Division of IT, Engineering and the EnvironmentsUniversity of South AustraliaAdelaideAustralia
  2. 2.Department of Applied Mathematics and Computational SciencePSG College of TechnologyCoimbatoreIndia

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