Statistical Baselines from Random Matrix Theory

Marotesa Voultsidou, J. Michael Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2008
Subtitle of host publication9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings
EditorsColin Fyfe, Dongsup Kim, Soo-Young Lee, Hujun Yin
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Electronic)978-3-540-88906-9
ISBN (Print)978-3-540-88905-2
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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