OBJECTIVE: Near-infrared spectroscopy (NiRS) is a noninvasive technology used in measuring oxy- and deoxy-hemoglobin changes, neural activation, functional connectivity, and vascular health assessment. In this paper, we propose a dynamic model of the NiRS signal to facilitate a better understanding of the underlying elements of this signal and as a means of validation for existing and new NiRS signal processing algorithms. METHODS: The model incorporates arterial pulsations, its possible frequency drifts and the reflected waves, the hemodynamic response function (HRF), Mayer waves, respiratory waves and other very low-frequency components of the NiRS signal. Parameter selection and model fitting have been carried out using measurements from a NiRS database. Our database includes 25 participants each with 64 channels, covering all the scalp and therefore providing realistic measures of the varying parameters. RESULTS: We compared synthetic resting-state and HRF-included model outputs with in vivo resting and task-included measurements. The results showed a significant equivalence of the in vivo and synthetic signals. CONCLUSION: The proposed signal model generates realistic NiRS signals. SIGNIFICANCE: The model accepts simple physiological and physical parameters to produce realistic NiRS signals and will accelerate the growth of optical signal processing algorithms.
OBJECTIVE: Near-infrared spectroscopy (NiRS) is a noninvasive technology used in measuring oxy- and deoxy-hemoglobin changes, neural activation, functional connectivity, and vascular health assessment. In this paper, we propose a dynamic model of the NiRS signal to facilitate a better understanding of the underlying elements of this signal and as a means of validation for existing and new NiRS signal processing algorithms. METHODS: The model incorporates arterial pulsations, its possible frequency drifts and the reflected waves, the hemodynamic response function (HRF), Mayer waves, respiratory waves and other very low-frequency components of the NiRS signal. Parameter selection and model fitting have been carried out using measurements from a NiRS database. Our database includes 25 participants each with 64 channels, covering all the scalp and therefore providing realistic measures of the varying parameters. RESULTS: We compared synthetic resting-state and HRF-included model outputs with in vivo resting and task-included measurements. The results showed a significant equivalence of the in vivo and synthetic signals. CONCLUSION: The proposed signal model generates realistic NiRS signals. SIGNIFICANCE: The model accepts simple physiological and physical parameters to produce realistic NiRS signals and will accelerate the growth of optical signal processing algorithms.