@article{c9fd046b58fa4062b3ba36499b2e3e87,
title = "A robust model for spatiotemporal dependencies",
abstract = "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.",
keywords = "Autodecorrelation, Blind source separation, Functional magnetic resonance imaging, Independent component analysis",
author = "Theis, {Fabian J.} and Peter Gruber and Keck, {Ingo R.} and Lang, {Elmar W.}",
note = "Funding Information: The authors gratefully acknowledge partial financial support by the DFG (GRK 638) and the BMBF (project {\textquoteleft}ModKog{\textquoteright}). They would like to thank D. Auer from the MPI of Psychiatry in Munich, Germany, for providing the fMRI data, and A. Meyer-B{\"a}se from the Department of Electrical and Computer Engineering, FSU, Tallahassee, USA for discussions concerning the fMRI analysis. The authors thank the anonymous reviewers for their helpful comments during preparation of this manuscript. ",
year = "2008",
month = jun,
doi = "10.1016/j.neucom.2007.06.012",
language = "English",
volume = "71",
pages = "2209--2216",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",
number = "10-12",
}