Key result
Transfer function models, spatial filtering, and adaptive filtering were promising for reducing physiological noise from oxy-Hb signals, but only TF models fit for deoxy-Hb signals.
Transfer function models are uniquely suited for reducing physiological noise in deoxy-Hb fNIRS signals, while spatial and adaptive filtering also work for oxy-Hb signals.
TF models may guide deoxy-Hb fNIRS denoising; hypothesis-generating and requires validation before clinical adoption.
In the present study we report on the reduction of physiological rhythms in hemodynamic ((de)oxy-Hb) signals recorded with functional near-infrared spectroscopy (fNIRS).We investigated the use of three different signal processing approaches (spatial filtering, adaptive filtering and transfer function (TF) models) to reduce the influence of respiratory and blood pressure rhythms on the hemodynamic responses.The results show that all three methods are promising for reducing the influence from oxy-Hb signals, but only TF models fit for deoxy-Hb signals.
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Bauernfeind et al. (2013) studied this question. Signal processing approaches (spatial filtering, adaptive filtering, transfer function models) was evaluated on Reduction of physiological rhythms in hemodynamic signals. Transfer function models, spatial filtering, and adaptive filtering were promising for reducing physiological noise from oxy-Hb signals, but only TF models fit for deoxy-Hb signals.
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