Joint blind source separation by multiset canonical correlation analysis

  • Authors:

  • Yi-Ou Li;Tülay Adali;Wei Wang;Vince D. Calhoun

  • Affiliations:

  • Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM and the Mind Research Network, Albuquerque, NM

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2009

Quantified Score

Hi-index 35.68

Visualization

Abstract

In this paper, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multiset canonical correlation analysis (M-CCA) [J. R. Kettenring, "Canonical analysis of several sets of variables," Biometrika, vol. 58, pp. 433-451, 1971]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task.