Joint Blind Source Separation by Multi-set Canonical Correlation Analysis

By Yi-Ou Li, T. Adalı, Wei Wang, Vince D. Calhoun. IEEE Transactions on Signal Processing, 2009

342 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bb7de3ebe5a6b18efb8b32dc126a17dcf43a5660

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Citations in the corpus

Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “and (ii) the corresponding latent variables are statistically independent [107], [108], both quite stringent and limiting assumptions.”
Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition (2016, methodology): “The feature level fusion techniques include the serial feature fusion [6], the parallel feature fusion [8], the CCA-based feature fusion [9], [41], and the most recently published JSRC [22] and SMDL [23] methods.”
A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data (2011, positive): “It can also be extended to multi-set CCA to incorporate more than two modalities (Correa et al., 2010a; Li et al., 2009).”
Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis (2013, methodology): “Recently, MsetCCA has been successfully applied to the joint blind source separation of multi-subject fMRI data 30 , 31 , the functional connectivity analysis of fMRI data 32 , and also the fusion of concurrent single trial ERP and fMRI data 33 .”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “second-order statistics (SOS) alone [110], [111].”