title: Localizing and estimating causal relations of interacting brain rhythms.
abstract: Estimating brain connectivity and especially causality between different brain regions from EEG or MEG is limited by the fact that the data are a largely unknown superposition of the actual brain activities. Any method, which is not robust to mixing artifacts, is prone to yield false positive results. We here review a number of methods that allow for addressing this problem. They are all based on the insight that the imaginary part of the cross-spectra cannot be explained as a mixing artifact. First, a joined decomposition of these imaginary parts into pairwise activities separates subsystems containing different rhythmic activities. Second, assuming that the respective source estimates are least overlapping, yields a separation of the rhythmic interacting subsystem into the source topographies themselves. Finally, a causal relation between these sources can be estimated using the newly proposed measure Phase Slope Index (PSI). This work, for the first time, presents the above methods in combination; all illustrated using a single, simulated data set.
authors.affiliation: Intelligent Data Analysis Group, Fraunhofer FIRST Berlin, Germany.
authors.forename: Guido
authors.initials: G
authors.lastname: Nolte
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller