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Estimating brain connectivity and especially causality betweendifferent brain regions from EEG or MEG is limited by the fact thatthe data are a largely unknown superposition of the actual brainactivities. Any method, which is not robust to mixing artifacts, isprone to yield false positive results. We here review a number ofmethods that allow to address this problem. They are all based on theinsight that the imaginary part of the cross-spectra cannot beexplained as a mixing artifact. First, a joined decomposition of theseimaginary parts into pairwise activities allows to separate subsystemscontaining different rhythmic activities. Second, assuming that therespective source estimates are least overlapping allows a separation of therhythmic interacting subsystem into the source topographiesthemselves. Finally, a causal relation between these sources can beestimated using the newly proposed measure Phase Slope Index (PSI).This work, for the first time, presents the above methods in combination;all applied to a single data set.