Joint Blind Source Separation by Multiset Canonical Correlation Analysis

• Published in: IEEE transactions on signal processing Vol. 57; no. 10; pp. 3918 - 3929
• Main Authors: Yi-Ou Li, Adali, T., Wei Wang, Calhoun, V.D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activation analysis; Applied sciences; Biological and medical sciences; Blind source separation; Blinds; Canonical correlation analysis; Computerized, statistical medical data processing and models in biomedicine; Correlation; Correlation analysis; Data analysis; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Frequency estimation; group analysis; Image analysis; Independent component analysis; Information, signal and communications theory; joint blind source separation; Magnetic analysis; Magnetic resonance imaging; Mathematical models; Maximization; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Separation; Signal and communications theory; Signal processing; Signal, noise; Source separation; Speech analysis; Speech enhancement; Studies; Tasks; Telecommunications and information theory; Performance evaluation; joint blind source separation; Separability; Parameter estimation; Source separation; Independent component analysis; Signal estimation; Functional analysis; Blind source separation; Nuclear magnetic resonance imaging; Canonical correlation; Complex variable method; Canonical analysis; Correlation analysis; Signal processing; Medical imagery; group analysis; Canonical correlation analysis; Functional imaging
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2009.2021636

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.