10.3389/fnhum.2018.00505.s004
Paola Pinti
Paola
Pinti
Felix Scholkmann
Felix
Scholkmann
Antonia Hamilton
Antonia
Hamilton
Paul Burgess
Paul
Burgess
Ilias Tachtsidis
Ilias
Tachtsidis
Table_4_Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework.DOCX
Frontiers
2019
functional near infrared spectroscopy
digital filter
general linear model
pre-processing standardization
functional data analysis
pre-processing guidelines
2019-01-11 04:32:58
Dataset
https://frontiersin.figshare.com/articles/dataset/Table_4_Current_Status_and_Issues_Regarding_Pre-processing_of_fNIRS_Neuroimaging_Data_An_Investigation_of_Diverse_Signal_Filtering_Methods_Within_a_General_Linear_Model_Framework_DOCX/7576406
<p>Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.</p>