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A robust model for spatiotemporal dependencies

Theis, Fabian J. ; Gruber, Peter ; Keck, Ingo R. ; Lang, Elmar


Real-world data sets such as recordings from functional magnetic resonance imaging (fMRI) often possess both spatial and temporal structures. Here, we propose an algorithm including such spatiotemporal information into the analysis, and reduce the problem to the joint approximate diagonalization of a set of autocorrelation matrices. We demonstrate the feasibility of the algorithm by applying it to fMRI analysis, where previous approaches are outperformed considerably.

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