Multivariate fusion of EEG and functional MRI data using ICA: Algorithm choice and performance analysis

Yuri Levin-Schwartz, Vince D. Calhoun, Tülay Adalı

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

Abstract

It has become common for neurological studies to gather data from multiple modalities, since the modalities examine complementary aspects of neural activity. Functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) data, in particular, enable the study of functional changes within the brain at different temporal and spatial scales; hence their fusion has received much attention. Joint independent component analysis (jICA) enables symmetric and fully multivariate fusion of these modalities and is thus one of the most widely used methods. In its application to jICA, Infomax has been the widely used, however the relative performance of Infomax is rarely shown on real neurological data, since the ground truth is not known. We propose the use of number of voxels in physically meaningful masks and statistical significance to assess algorithm performance of ICA for jICA on real data and show that entropy bound minimization (EBM) provides a more attractive solution for jICA of EEG and fMRI.

Original languageEnglish (US)
Title of host publicationLatent Variable Analysis and Signal Separation - 12th International Conference, LVA/ICA 2015, Proceedings
EditorsZbynĕk Koldovský, Emmanuel Vincent, Arie Yeredor, Petr Tichavský
PublisherSpringer Verlag
Pages489-496
Number of pages8
ISBN (Print)9783319224817
DOIs
StatePublished - 2015
Externally publishedYes
Event12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2015 - Liberec, Czech Republic
Duration: Aug 25 2015Aug 28 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9237
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2015
Country/TerritoryCzech Republic
CityLiberec
Period8/25/158/28/15

Keywords

  • Data fusion
  • EEG
  • FMRI
  • Independent component analysis
  • Medical imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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