Detecting Microstate Transition in Human Brain via Eigenspace of Spatiotemporal Graph

In this letter, a novel approach for detecting the transition of electroencephalography (EEG) microstates of the human brain has been proposed. We have considered the EEG electrodes as nodes of a graph and the correlation between the electrodes' signals as the edge weights. Then, using spectral analysis of the graph, a method has been proposed for detecting the transition of the microstates. The proposed method is comprised of two steps. First, a spatiotemporal graph is constructed using the correlation between the Laplacian of the spatial graph at consecutive time instants. Then, using the principal angles of the eigenspace of the graph, the transition of the EEG microstate has been detected. Experimental results on two publicly available datasets show that the proposed method performs more accurately than state-of-the-art. On the first dataset for the 15 out of 20 subjects, the spatiotemporal graph approach improved the accuracy of detecting the transition time. On the second dataset, it missed only two transitions as opposed to the spatial graph, which failed to detect overall ten transitions across all the subjects.