EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism

  • Vangelis SakkalisEmail author
  • Konstantinos Marias
Part of the Fields Institute Communications book series (FIC, volume 63)


Over the past few years there has been an increased interest in studying the under-lying neural mechanism of cognitive brain activity in order to identify features capable of discriminating brain engagement tasks in terms of cognitive load. Rather recently there is a growing suspicion that the noninvasive technique of high-resolution quantitative electroencephalography may provide features able to identify and quantify functional interdependencies among synchronized brain lobes based on graph-theoretic algorithms. In the emerging view of translational medicine, graph-theoretic measures and tools, currently used to describe large scale networks, can be potential candidates for future inclusion in a clinical trial setting. This paper discusses different families of graph theoretical measures able to capture the topology of brain networks as potential EEG-based biomarkers. As a case study, statistically significant graph-theoretic indices, capable of capturing and quantifying collective motifs in an alcoholism paradigm are identified and presented.


Functional Connectivity Generalize Synchronization Adjacency Matrice Characteristic Path Length Wavelet Coherence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Henri Begleiter at the Neurodynamics Laboratory, State University of NY Health Center at Brooklyn for kindly providing the EEG dataset.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Institute of Computer ScienceFoundation for Research and TechnologyHeraklionGreece

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