  In the current paper, we introduce a parametric data-driven model for
functional near-infrared spectroscopy that decomposes a signal into a series of
independent, rescaled, time-shifted, hemodynamic basis functions. Each
decomposed waveform retains relevant biological information about the expected
hemodynamic behavior. The model is also presented along with an efficient
iterative estimation method to improve the computational speed. Our hemodynamic
decomposition model (HDM) extends the canonical model for instances when a) the
external stimuli are unknown, or b) when the assumption of a direct
relationship between the experimental stimuli and the hemodynamic responses
cannot hold. We also argue that the proposed approach can be potentially
adopted as a feature transformation method for machine learning purposes. By
virtue of applying our devised HDM to a cognitive load classification task on
fNIRS signals, we have achieved an accuracy of 86.20%+-2.56% using six channels
in the frontal cortex, and 86.34%+-2.81% utilizing only the AFpz channel also
located in the frontal area. In comparison, state-of-the-art time-spectral
transformations only yield 64.61%+-3.03% and 37.8%+-2.96% under identical
experimental settings.
