  With the advances in high resolution neuroimaging, there has been a growing
interest in the detection of functional brain connectivity. Complex network
theory has been proposed as an attractive mathematical representation of
functional brain networks. However, most of the current studies of functional
brain networks have focused on the computation of graph theoretic indices for
static networks, i.e. long-time averages of connectivity networks. It is
well-known that functional connectivity is a dynamic process and the
construction and reorganization of the networks is key to understanding human
cognition. Therefore, there is a growing need to track dynamic functional brain
networks and identify time intervals over which the network is
quasi-stationary. In this paper, we present a tensor decomposition based method
to identify temporally invariant 'network states' and find a common topographic
representation for each state. The proposed methods are applied to
electroencephalogram (EEG) data during the study of error-related negativity
(ERN).
