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Activity Number: 257 - SPEED: Longitudinal/Correlated Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 2:45 PM
Sponsor: ENAR
Abstract #332598
Title: Coherence-Based Time Series Clustering for Brain Connectivity Visualization
Author(s): Carolina Euan Campos* and Ying Sun and Hernando Ombao
Companies: KAUST and KAUST and King Abdullah University of Science and Technology
Keywords: Cluster Coherence; Multivariate Time Series; Electroencephalograms; Spectral Analysis; Classification; Brain Connectivity

We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display a high level of coordination as measured by "cluster-coherence." While the most common approach to measuring dependence between clusters is through pairs of single time series, our method proposes cluster coherence which measures dependence between whole clusters rather than between single elements. Thus it takes into account both the dependence between clusters and within channels in a cluster. The identified clusters contain time series that exhibit high cross-dependence in the spectral domain. That is, these clusters correspond to connected brain regions with synchronized oscillatory activity. In the simulation studies, we show that the proposed HCC outperforms commonly used clustering algorithms, such as average coherence and minimum coherence based methods. To study clustering in a network of multichannel electroencephalograms (EEG) during an epileptic seizure, we applied the HCC method and identified connectivity in alpha (8 ? 12) Hertz and beta (16 ? 30) Hertz bands at different phases.

Authors who are presenting talks have a * after their name.

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