This paper presents a novel method for signal localization for building high-performance brain-computer interfaces using Near-Infrared Spectroscopy. It first proposes a kernel-based model to represent haemodynamic signals of interest under parameterized transformations. A mathematical solution is therefore derived to locate the signals by estimating the parameters. We employ a support vector machine to classify the located signals into left/right hand movements. We evaluate the method on both simulated and real world data, with positive results suggesting the method?s high efficacy. This work can be extended to other systems using e.g. fMRI and EEG.