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

Petros Xanthopoulos; Ashwin Arulselvan; Panos M. Pardalos

doi:10.1007/978-0-387-88619-0_10

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

Petros Xanthopoulos, Ashwin Arulselvan, Panos M. Pardalos

Management Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Sensors
Subtitle of host publication	Theory, Algorithms, and Applications
Editors	Vladimir L. Boginski, Clayton W. Commander, Panos M. Pardalos, Yinyu Ye
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	231-242
Number of pages	12
ISBN (Print)	9780387886183
DOIs	https://doi.org/10.1007/978-0-387-88619-0_10
Publication status	Published - 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

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A spectral clustering approach for modeling connectivity patterns in electroencephalogram sensor networks. / Xanthopoulos, Petros; Arulselvan, Ashwin; Pardalos, Panos M.

Sensors: Theory, Algorithms, and Applications. ed. / Vladimir L. Boginski; Clayton W. Commander; Panos M. Pardalos; Yinyu Ye. Cham, Switzerland : Springer, 2011. p. 231-242.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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Access to Document

10.1007/978-0-387-88619-0_10

https://www.springer.com/gp/book/9780387886183