Accurate hemodynamic response estimation by removal of stimulus-evoked superficial response in fNIRS signals.
Objective: We address the problem of hemodynamic response estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the hemodynamic response estimation, therefore careful and accurate denoising of data is needed. Approach: We propose a dictionary-based algorithm to process every single event-related segment of the acquired signal for both long separation and short separation channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the short-separation channel is employed to correct for the evoked superficial response and refine the hemodynamic response estimate from the long-separation channel.
Main results: Analysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for HbO and 78% for HbR, considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial hemodynamic response estimation, a significant reduction in the number of averaged trials can also be obtained. Significance: This work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. It demonstrates the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based hemodynamic response analysis.
PMID: 33440365 [PubMed – as supplied by publisher]
J Neural Eng. 2021 Jan 13;:
Authors: Galli A, Brigadoi S, Giorgi G, Sparacino G, Narduzzi C