Statistical Baselines from Random Matrix Theory

Authors:
Marotesa Voultsidou;J. Michael Herrmann
Affiliations:
Department of Physics, University of Crete, Heraklion, Greece;Institute for Perception, Action and Behaviour Informatics Forum, University of Edinburgh, Edinburgh, U.K. EH8 9AB
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

Citing 2
Cited 0

Testing for nonlinearity in time series: the method of surrogate data

Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Feature evaluation in fMRI data using random matrix theory

Computing and Visualization in Science

Quantified Score

Hi-index	0.00

Visualization

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.