Feature-selective ICA and its convergence properties

We present a projection-based framework for a feature-selective independent component analysis (FS-ICA) scheme and study its convergence property for two ICA algorithms, FastICA and Infomax. As examples, we implement band-pass filter as the featureselective filter to improve the estimation of a bandpass signal from the mixtures and a periodic task-related time course embedded in the functional Magnetic Resonance Imaging (fMRI) data. Hence, we demonstrate that the proposed method can incorporate a priori information into ICA to effectively improve estimation of the underlying components of practical interest, such as periodic time courses and smooth brain activation areas in fMRI data.