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Simultaneous EEG and fMRIRecording, Analysis, and Application$
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Markus Ullsperger and Stefan Debener

Print publication date: 2010

Print ISBN-13: 9780195372731

Published to Oxford Scholarship Online: May 2010

DOI: 10.1093/acprof:oso/9780195372731.001.0001

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Using ICA for the Analysis of fMRI Data

Using ICA for the Analysis of fMRI Data

Chapter:
(p.135) 3.2 Using ICA for the Analysis of fMRI Data
Source:
Simultaneous EEG and fMRI
Author(s):

Giancarlo Valente

Fabrizio Esposito

Federico de Martino

Rainer Goebel

Elia Formisano

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195372731.003.0009

This chapter examines the most relevant aspects concerning the use of independent component analysis (ICA) for the analysis of functional magnetic resonance imaging (fMRI) data. In particular, after illustrating the fMRI-ICA model (“Problem formulation and application to fMRI”), the chapter compares the most commonly used ICA algorithms in the context of fMRI data analysis. The problems of choosing the dimensionality of the ICA decomposition, and of selecting the “meaningful” components, are considered. Optimizations of the ICA algorithms for dealing with the specific spatiotemporal properties of the fMRI data, and extensions of the ICA to multisubject fMRI studies, are described. For each of these aspects, different approaches from various groups are briefly reviewed.

Keywords:   EEG, independent component analysis, functional magnetic resonance, fMRI

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