Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

Abstract

Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.

Hemodynamic Activity and Connectivity of the Prefrontal Cortex by Using Functional Near-Infrared Spectroscopy during Color-Word Interference Test in Korean and English Language

“…The wavelet-based filtering and the hemodynamic response function (HRF) were the only utilized during the group analysis using NIRS-SPM, another MATLAB-based software. No other artifact correction was used since the use of HRF reportedly produces the highest increase in contrast-to-noise ratio (CNR) by about 39% [31][32][33][34]. Moreover, this was also in the study of Srikanth and Ramakrishnan [35] in identifying the regions of the brain that are activated through the strength of the amplitude for each task given.…”

OptoNet II: An Advanced MATLAB-Based Toolbox for Functional Cortical Connectivity Analysis With Surrogate Tests Using fNIRS

“…However, GLM-based analysis methods often fail to adequately analyze brain functions because of artifacts in the fNIRS measurements. The artifacts exist for various reasons, such as subject movements, blood pressure variations, and instrumental instabilities [14][15][16][17][18][19]. Recently, various connection and causality estimation methods for functional brain network analysis have been developed, and have demonstrated their utility in cognitive neuroscience and neurological clinical studies [20].…”

OptoNet: a MATLAB-based toolbox for cortical network analyses using functional near-infrared spectroscopy

“…Recently, numerous algorithms based on wavelet transform, signal correlation, and artificial neural networks have been adopted for fNIRS signal analysis. 19,[22][23][24][25][26][27] Although these algorithms yield good results, any excess preprocessing leads to attenuation of the hemodynamic response. Therefore, the research interests of fNIRS need to migrate to other analysis methods, such as brain functional connectivity and causality, which are important to achieve a better understanding of brain functions and medical approaches.…”