In this paper, we consider an extension of independent component analysis (ICA) and blind source separation (BSS) techniques to several related data sets. The goal is to separate mutually dependent and independent components or source signals from these data sets. This problem is important in practice, because such data sets are common in real-world applications. We propose a new method which first uses a generalization of standard canonical correlation analysis (CCA) for detecting subspaces of independent and dependent components. Any ICA or BSS method can after this be used for final separation of these components. The proposed method performs well for synthetic data sets for which the assumed data model holds, and provides interesting and meaningful results for real-world functional magnetic resonance imaging (fMRI) data. The method is straightforward to implement and computationally not too demanding. The proposed method improves clearly the separation results of several well-known ICA and BSS methods compared with the situation in which generalized CCA is not used.
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