The 2011 Signal Separation Evaluation Campaign (SiSEC2011): - Biomedical Data Analysis -

  • Guido Nolte
  • Dominik Lutter
  • Andreas Ziehe
  • Francesco Nesta
  • Emmanuel Vincent
  • Zbyněk Koldovský
  • Alexis Benichoux
  • Shoko Araki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)

Abstract

This paper summarizes the bio part of the 2011 community based Signal Separation Evaluation Campaign (SiSEC2011). Two different data sets were given. In the first task, participants were asked to estimate the causal relations of underlying sources from simulated bivariate EEG data. In the second task, participants were asked to reconstruct signaling pathways or parts of it from the microarray expression profiles. The results for each task were evaluated using different objective performance criteria. We provide an overview of the biomedical datasets, tasks and criteria, and we report on the achieved results.

Keywords

Granger Causality Independent Component Analysis Blind Source Separation Transfer Entropy Directed Transfer Function 
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 2012

Authors and Affiliations

  • Guido Nolte
    • 1
  • Dominik Lutter
    • 2
  • Andreas Ziehe
    • 3
  • Francesco Nesta
    • 4
  • Emmanuel Vincent
    • 5
  • Zbyněk Koldovský
    • 6
  • Alexis Benichoux
    • 5
  • Shoko Araki
    • 7
  1. 1.Fraunhofer Institute FIRSTGermany
  2. 2.IBISHelmholtz Zentrum MünchenGermany
  3. 3.Technical University BerlinGermany
  4. 4.Fondazione Bruno Kessler - Irst, Center of Information TechnologyItaly
  5. 5.Centre Inria RennesINRIABretagne AtlantiqueFrance
  6. 6.Technical University of LiberecCzech Republic
  7. 7.NTT Communication Science Labs.NTT CorporationJapan

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