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Analysis of Neural Data

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Regularized Second Order Source Separation
Citation key Schiessl2000c
Author Schießl, I. and Schöner, H. and Stetter, M. and Dima, A. and Obermayer, K.
Title of Book Int. Workshop on Independent Component Analysis and Blind Signal Separation
Pages 111 – 116
Year 2000
Volume 2
Editor P. Pajunen and J. Karhunen
Abstract In the separation task of linear mixtures from real experiments the dependencies of the original sources often make \"classical\" independent component analysis (ICA) algorithms fail. One way to overcome this drawback is the introduction of additional knowledge we have about the mixing process. We introduce a regularization term to the cost function of multishift extended spatial decorrelation (multishift ESD) that punishes the deviation of the time course of the estimated sources from a assumed time course during an experiment. In the case of optical imaging such knowledge can be achieved from the metabolic response of signals to the stimulus onset. We show how the regularization term improves the separation result at different noise levels. The simulations were run on a artificial toy dataset and one dataset that contains prototype signals from a real optical imaging experiment.
Bibtex Type of Publication Selected:sources
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