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In the current paper, we introduce a parametric data-driven model forfunctional near-infrared spectroscopy that decomposes a signal into a series ofindependent, rescaled, time-shifted, hemodynamic basis functions. Eachdecomposed waveform retains relevant biological information about the expectedhemodynamic behavior. The model is also presented along with an efficientiterative estimation method to improve the computational speed. Our hemodynamicdecomposition model (HDM) extends the canonical model for instances when a) theexternal stimuli are unknown, or b) when the assumption of a directrelationship between the experimental stimuli and the hemodynamic responsescannot hold. We also argue that the proposed approach can be potentiallyadopted as a feature transformation method for machine learning purposes. Byvirtue of applying our devised HDM to a cognitive load classification task onfNIRS signals, we have achieved an accuracy of 86.20%+-2.56% using six channelsin the frontal cortex, and 86.34%+-2.81% utilizing only the AFpz channel alsolocated in the frontal area. In comparison, state-of-the-art time-spectraltransformations only yield 64.61%+-3.03% and 37.8%+-2.96% under identicalexperimental settings.

A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy