Spatially constrained ICA algorithm with an application in EEG processing

Authors:
Maarten De Vos;Lieven De Lathauwer;Sabine Van Huffel
Affiliations:
Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee, Leuven, Belgium and Neuropsychology laboratory, Department of Psychology, Unive ...;Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee, Leuven, Belgium and Katholieke Universiteit Leuven Campus Kortrijk, Group Scien ...;Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee, Leuven, Belgium
Venue:
Signal Processing
Year:
2011

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Abstract

Independent Component Analysis (ICA) aims at blindly decomposing a linear mixture of independent sources. It has lots of applications in diverse research areas. In some applications, there is prior knowledge on the sources and/or the mixing vectors. This prior knowledge can be incorporated in the computation of the independent sources. In this paper we provide an algorithm for so-called spatially constrained ICA (scICA). The algorithm deals with the situation when one mixing vector is exactly known. Also the generalization to more mixing vectors is discussed. Numerical experiments are reported that allow us to assess the improvement in accuracy that can be achieved with these algorithms compared to fully blind ICA and to a previously proposed constrained algorithm. We illustrate the approach with a biomedical application.