Graph Averaging as a Means to Compare Multichannel EEG Coherence Networks

Abstract

A method is proposed for quantifying differences between multichannel EEG coherence networks represented by functional unit (FU) maps. The approach is based on inexact graph matching for attributed relational graphs and graph averaging, adapted to FU maps. The mean of a set of input FU maps is defined in such a way that it not only represents the mean group coherence during a certain task or condition but also to some extent displays individual variations in brain activity. The definition of a mean FU map relies on a graph dissimilarity measure which takes into account both node positions and node or edge attributes. A visualization of the mean FU map is used with a visual representation of the frequency of occurrence of nodes and edges in the input FUs. This makes it possible to investigate which brain regions are more commonly involved in a certain task, by analysing the occurrence of an FU of the mean graph in the input FUs. Furthermore, our method gives the possibility to quantitatively compare individual FU maps by computing their distance to the mean FU map.