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.
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Xanthopoulos, P., Arulselvan, A., Pardalos, P.M. (2012). A Spectral Clustering Approach for Modeling Connectivity Patterns in Electroencephalogram Sensor Networks. In: Boginski, V.L., Commander, C.W., Pardalos, P.M., Ye, Y. (eds) Sensors: Theory, Algorithms, and Applications. Springer Optimization and Its Applications(), vol 61. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88619-0_10
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