We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear

mixtures when some a priori information of the sources are available in the form of rough templates (references). The constrained

independent component analysis (cICA) is extended to incorporate the reference signals that carry some information of the sources as

additional constraints into the ICA contrast function. A neural algorithm is then proposed using a Newton-like approach to obtain an

optimal solution to the constrained optimization problem. Stability of the convergence and selection of parameters in the learning

algorithm are analyzed. Experiments with synthetic signals and real fMRI data demonstrate the efficacy and accuracy of the proposed

algorithm.