A Kernel-based Signal Localization Method for NIRS Brain-computer Interfaces

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