CCA for joint blind source separation of multiple datasets with application to group fMRI analysis

Yi Ou Li, Wei Wang, Tülay Adali, Vince D. Calhoun

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

Abstract

In this work, we propose a scheme for joint blind source separation (BSS) of multiple datasets using canonical correlation analysis (CCA). The proposed scheme jointly extracts sources from each dataset in the order of between-set source correlations. We show that, when sources are uncorrelated within each dataset and correlated across different datasets only on corresponding indices, (i) CCA on two datasets achieves BSS when the sources from the two datasets have distinct between-set correlation coefficients, and (ii) CCA on multiple datasets (M-CCA) achieves BSS with a more relaxed condition on the between-set source correlation coefficients compared to CCA on two datasets. We present simulation results to demonstrate the properties of CCA and M-CCA on joint BSS. We apply M-CCA to group functional magnetic resonance imaging (fMRI) data acquired from several subjects performing a visuomotor task and obtain interesting brain activations as well as their correlation profiles across different subjects in the group.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1837-1840
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Blind source separation
  • Canonical correlation analysis
  • Group analysis
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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