A Spectral Clustering Approach for Modeling Connectivity Patterns in Electroencephalogram Sensor Networks

  • Petros Xanthopoulos
  • Ashwin Arulselvan
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 61)


Electroencephalography (EEG) is a non-invasive low cost monitoring exam that is used for the study of the brain in every hospital and research labs. Time series recorded from EEG sensors can be studied from the perspective of computational neuroscience and network theory to extract meaningful features of the brain. In this chapter we present a network clustering approach for studying synchronization phenomena as captured by cross-correlation in EEG recordings. We demonstrate the proposed clustering idea in simulated data and in EEG recordings from patients with epilepsy.


Sensor Network Spectral Cluster Absence Epilepsy Binary Constraint Synchronization Measure 
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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Petros Xanthopoulos
    • 1
  • Ashwin Arulselvan
    • 2
  • Panos M. Pardalos
    • 1
  1. 1.Center for Applied Optimization, Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Technische Universität BerlinBerlinGermany

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