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.

中文翻译：

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通过多集典型相关分析进行联合盲源分离

在本文中，我们介绍了一种简单有效的方案，使用多集规范相关分析 (M-CCA) [JR Kettenring，“多组变量的规范分析”，Biometrika，卷. 58，第 433-451 页，1971 年]。我们首先提出了一个基于数据集内部和数据集之间潜在源相关性的联合 BSS 生成模型。我们指定源可分离性条件，并表明，当满足条件时，M-CCA 可以通过提取源之间的相关性最大化来联合提取每个数据集中对应源的组。我们将 M-CCA 方案的源分离性能与其他联合 BSS 方法进行了比较，并证明了 M-CCA 方案在实现大量数据集、具有异构相关值的相应源组和复杂的联合 BSS 方面的优越性能。具有圆形和非圆形分布的有价值的源。我们将 M-CCA 应用于分析来自多个受试者的功能性磁共振成像 (fMRI) 数据，并展示其在从视觉运动任务中估计有意义的大脑激活中的效用。