A spectral clustering approach for modeling connectivity patterns in electroencephalogram sensor networks

Petros Xanthopoulos, Ashwin Arulselvan, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.
Original languageEnglish
Title of host publicationSensors
Subtitle of host publicationTheory, Algorithms, and Applications
EditorsVladimir L. Boginski, Clayton W. Commander, Panos M. Pardalos, Yinyu Ye
Place of PublicationCham, Switzerland
PublisherSpringer
Pages231-242
Number of pages12
ISBN (Print)9780387886183
DOIs
Publication statusPublished - 24 Nov 2011

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Keywords

  • sensor network
  • spectral cluster
  • absence epilepsy
  • binary constraint
  • synchronization measure

Cite this

Xanthopoulos, P., Arulselvan, A., & Pardalos, P. M. (2011). A spectral clustering approach for modeling connectivity patterns in electroencephalogram sensor networks. In V. L. Boginski, C. W. Commander, P. M. Pardalos, & Y. Ye (Eds.), Sensors: Theory, Algorithms, and Applications (pp. 231-242). Cham, Switzerland: Springer. https://doi.org/10.1007/978-0-387-88619-0_10