In this paper, we propose a graph-theoretical approach to reveal patterns of functional dependencies between different scalp regions. We start by computing pairwise measures of dependence from dense-array scalp electroencephalographic (EEG) recordings. The obtained dependence matrices are then averaged over trials and further statistically processed to provide more reliability. Graph structure information is subsequently extracted using several graph theoretical measures. Simple measures of node degree and clustering strength are shown to be useful to describe the global properties of the analyzed networks. More sophisticated measures, such as betweenness centrality and subgraph centrality tend to provide additional insight into the network structure, and therefore robustly discriminate two cognitive states. We further examine the connected components of the graph to identify the dependent functional regions. The approach supports dynamicity in that all suggested computations can be easily extended to different points in time, thus enabling to monitor dependence evolution and variability with time.