Blind separation of spatio-temporal data sources

ICA and similar techniques have been previously applied to either one-dimensional signals or still images. We consider the problem of blind separation of dynamic sources, i.e. functions of both time and two spatial variables. We extend the Sparse ICA (SPICA) approach and apply it to a sliding data cube, defined by the two dimensions of the visual scene and the extent in time over which the mixing problem can be considered to be stationary and linear. This framework and formalism are applied to two special problems encountered in two different fields: The first deals with separation of dynamic reflections from a desired moving visual scene, without having any a priori knowledge on the structure of the images and/or their statistics. The second problem concerns blind separation of 'neural cliques' from the background firing activity of a neural network. The approach is generic in that it is applicable to any linearly mixed dynamic sources.