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Robust Second-Order Source Separation Identifies Experimental Responses in Biomedical Imaging

  • Fabian J. Theis
  • Nikola S. Müller
  • Claudia Plant
  • Christian Böhm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6365)

Abstract

Multidimensional biomedical imaging requires robust statistical analyses. Corresponding experiments such as EEG or FRAP commonly result in multiple time series. These data are classically characterized by recording response patterns to any kind of stimulation mixed with any degree of noise levels. Here, we want to detect the underlying signal sources such as these experimental responses in an unbiased fashion, and therefore extend and employ a source separation technique based on temporal autodecorrelation. Our extension first centers the data using a multivariate median, and then separates the sources based on approximate joint diagonalization of multiple sign autocovariance matrices.

Keywords

Independent Component Analysis Scatter Matrice Spatial Median Robust Covariance Multivariate Median 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabian J. Theis
    • 1
  • Nikola S. Müller
    • 2
  • Claudia Plant
    • 3
  • Christian Böhm
    • 4
  1. 1.IBIS, Helmholtz Zentrum MunichGermany
  2. 2.Max Planck Institute for BiochemistryMartinsriedGermany
  3. 3.Florida State UniversityUSA
  4. 4.University of MunichGermany

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