Multidimensional independent component analysis

This paper proposes to generalize the notion of independent component analysis (ICA) to the notion of multidimensional independent component analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces. From this standpoint, the BSS/ICA model is generalized to multidimensional components. We discuss how ICA standard algorithms can be adapted to MICA decomposition. The relevance of these ideas is illustrated by a MICA decomposition of ECG signals.

[1]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[2]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[3]  L. Lathauwer,et al.  Fetal electrocardiogram extraction by source subspace separation , 1995 .

[4]  Asoke K. Nandi,et al.  Foetal ECG extraction using blind source separation methods , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[5]  Bart De Schutter,et al.  DAISY : A database for identification of systems , 1997 .

[6]  Jean-François Cardoso,et al.  Estimating equations for source separation , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.