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Statistical Baselines from Random Matrix Theory

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Intelligent Data Engineering and Automated Learning – IDEAL 2008 (IDEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5326))

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Abstract

Quantitative descriptors of intrinsic properties of imaging data can be obtained from the theory of random matrices (RMT). Based on theoretical results for standardized data, RMT offers a systematic approach to surrogate data which allows us to evaluate the significance of deviations from the random baseline. Considering exemplary fMRI data sets recorded at a visuo-motor task and rest, we show their distinguishability by RMT-based quantities and demonstrate that the degree of sparseness and of localization can be evaluated in a strict way, provided that the data are sufficiently well described by the pairwise cross-correlations.

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Author information

Authors and Affiliations

  1. Department of Physics, University of Crete, P.O. Box 2208, Heraklion, Crete, Greece

    Marotesa Voultsidou

  2. Institute for Perception, Action and Behaviour Informatics Forum, University of Edinburgh, 11 Crichton Street, Edinburgh, EH8 9AB, U.K.

    J. Michael Herrmann

Authors
  1. Marotesa Voultsidou
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  2. J. Michael Herrmann
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Editor information

Editors and Affiliations

  1. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

  2. KAIST, Daejeon, Korea

    Dongsup Kim

  3. Brain Science Research Center and Department of Bio & Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea

    Soo-Young Lee

  4. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2008 Springer-Verlag Berlin Heidelberg

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Voultsidou, M., Herrmann, J.M. (2008). Statistical Baselines from Random Matrix Theory. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_46

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  • DOI: https://doi.org/10.1007/978-3-540-88906-9_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88905-2

  • Online ISBN: 978-3-540-88906-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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