Statistical Baselines from Random Matrix Theory

Marotesa Voultsidou, J. Michael Herrmann

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

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.
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
Pages362-369
Number of pages8
ISBN (Electronic)978-3-540-88906-9
ISBN (Print)978-3-540-88905-2
DOIs
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume5326
ISSN (Print)0302-9743

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