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In this paper, we present a simple method to find networks of time-correlated brain sources, using a singular value decomposition (SVD) analysis of the source matrix estimated after any linear distributed inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Despite the high dimension of the source space, our method allows for the rapid computation of the source matrix. In order to do this, we use the linear relationship between sensors and sources, and show that the SVD can be calculated through a simple and fast computation. We show that this method allows the estimation of one or several global networks of correlated sources without calculating a coupling coefficient between all pairs of sources. A series of simulations studies were performed to estimate the efficiency of the method. In order to illustrate the validity of this approach in experimental conditions, we used real MEG data from a visual stimulation task on one test subject and estimated, in different time windows of interest, functional networks of correlated sources.