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 measure dependence between clusters is through pairs of single time series, our method proposes cluster coherence which measures dependence between pairs of 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. Simulation studies demonstrate that the proposed HCC method is competitive with the other feature-based clustering methods. To study clustering in a network of multichannel electroencephalograms (EEG) during an epileptic seizure, we applied the HCC method and identified connectivity on alpha $(8,12)$ Hertz and beta $(16,30)$ Hertz bands at different phases of the recording: before an epileptic seizure, during the early and middle phases of the seizure episode. To increase the potential impact of HCC in neuroscience, we also developed the HCC-Vis, an R-Shiny app (RStudio), which can be downloaded from https://carolinaeuan.shinyapps.io/hcc-vis/.
"Coherence-based time series clustering for statistical inference and visualization of brain connectivity." Ann. Appl. Stat. 13 (2) 990 - 1015, June 2019. https://doi.org/10.1214/18-AOAS1225