Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research.

Patrick W Dans,Stevie D Foglia,Aimee J Nelson

doi:10.3390/brainsci11050606

Abstract

FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

Highlights

Functional near-infrared spectroscopy is a form of neuroimaging that utilizes light in the near-infrared range (700–1000 nm) to measure concentration changes in hemoglobin present in the cortex [1]
In Functional near-infrared spectroscopy (fNIRS), photons of light are projected into the scalp by the source optode and pass through the skull and into the upper cortical regions
The most frequently used pre-processing techniques were identified as bandpass filter, low-pass filter, high-pass filter, smoothing algorithms, and wavelet filtering

Summary

Introduction

Functional near-infrared spectroscopy (fNIRS) is a form of neuroimaging that utilizes light in the near-infrared range (700–1000 nm) to measure concentration changes in hemoglobin present in the cortex [1]. Once the available substrate in the region is utilized, sustained brain activity is dependent on the ability of vascular channels to supply cortical regions with blood rich in oxygen and glucose [2] Once these available substrates are reduced cerebral blood flow to that region increases through local arterial vasodilation, a process known as neurovascular coupling [3]. In fNIRS, photons of light are projected into the scalp by the source optode and pass through the skull and into the upper cortical regions These photons are scattered and reflected as they travel through the head. The modified Beer–Lambert law is used to quantify changes in oxy-Hb and deoxy-Hb as a result of neurovascular coupling (see Figure 1b) In this equation, optical density (OD) is equal to the negative log of the attenuated light intensity (I) over the initial light intensity (I0). These terms are multiplied by the differential pathlength factor (DPF), which accounts

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