Tracking and Visualizing Dynamic Structures in Multichannel EEG Coherence Networks

Abstract

An electroencephalography (EEG) coherence network represents functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of coherence networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists understand brain mechanisms. However, most studies have been limited to static EEG coherence networks or were focused on individual network nodes. In this poster, we consider groups of nodes for visualizing the evolution of network communities and their corresponding spatial location. We use a timeline-based representation to provide an overview of the evolution of functional units (FUs) and their corresponding spatial location over time. This representation can help the viewer identify functional units across the whole time window, as well as to identify relations between functional units and brain regions. In addition, a time-annotated FU map is provided to facilitate comparison of the behavior of the nodes between consecutive FU maps. This time-annotated FU map provides more detail about how the classification of electrodes into FUs changes over time. Our method is proposed as a first step towards a complete analysis of EEG coherence networks.