We consider an extension of ICA and BSS for separating

mutually dependent and independent components from two related data

sets. We propose a new method which first uses canonical correlation

analysis for detecting subspaces of independent and dependent components.

Different ICA and BSS methods can after this be used for final

separation of these components. Our method has a sound theoretical

basis, and it is straightforward to implement and computationally not

demanding. Experimental results on synthetic and real-world fMRI data

sets demonstrate its good performance.