Independent component analysis with feature selective filtering

In this contribution, we propose a feature selective filtering scheme for independent component analysis (ICA) to improve the estimation of the sources of interest (SOI), i.e., sources that have certain desired features in their sample space. As an example, we show that ICA with a smooth filtering scheme can improve the estimation of the smooth image sources from a mixture of images, as well as the estimation of a smooth visual activation map in a hybrid functional magnetic resonance imaging (fMRI) data set. Hence, the technique can potentially be used in the analysis of fMRI data to improve the ICA estimation of functional activation regions that are expected to be smooth