A robust model for spatiotemporal dependencies

Authors:
Fabian J. Theis;Peter Gruber;Ingo R. Keck;Elmar W. Lang
Affiliations:
Bernstein Center for Computational Neuroscience Max-Planck-Institute for Dynamics and Self-Organisation, Göttingen, Germany and Institute of Biophysics, University of Regensburg, Regensburg, ...;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany
Venue:
Neurocomputing
Year:
2008

Citing 6
Cited 3

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Jacobi Angles for Simultaneous Diagonalization

SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources

IEEE Transactions on Signal Processing

Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure

IEEE Transactions on Circuits and Systems Part I: Regular Papers
Activation detection on fMRI time series using hidden Markov model

Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Brain connectivity analysis: a short survey

Computational Intelligence and Neuroscience

Quantified Score

Hi-index	0.02

Visualization

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.