Subspace analysis for characterizing dynamic functional brain networks

Human brain is known to be one of the most complex biological systems and understanding the functional connectivity patterns to distinguish between normal and disrupted brain behavior still remains a challenge. Previous studies focus on analyzing functional connectivity averaged over a certain time and frequency window which is generally not sufficient to address the time-varying evolution of the connectivity patterns. In this paper, we propose a framework to describe the dynamic properties of functional connectivity in the brain. The proposed approach is based on constructing time-varying connectivity graphs from multichannel electroencephalogram (EEG) data, using subspace analysis to detect network-wide changes, identifying key event intervals and then extracting representative networks that describe the connectivity in each event interval. This framework is evaluated for EEG data, containing error-related negativity (ERN) component related to cognitive control. For each time interval, the statistically significant connectivity patterns are presented to illustrate the dynamic nature of functional connectivity.