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Front. Hum. Neurosci. | doi: 10.3389/fnhum.2018.00505

Current status and issues regarding pre-processing of fNIRS neuroimaging data: An investigation of diverse signal filtering methods within a General Linear Model framework

  • 1University College London, United Kingdom
  • 2University of Zurich, Switzerland

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 towards improving the analysis of fNIRS data and dissemination of the results.

Keywords: Functional Near Infrared Spectroscopy (fNIRS), Digital filter, General Linear Model (GLM), Pre-processing standardization, functional data analysis, Pre-processing guidelines

Received: 24 Sep 2018; Accepted: 03 Dec 2018.

Edited by:

Stephane Perrey, Université de Montpellier, France

Reviewed by:

Noman Naseer, Air University, Pakistan
Abdul Rauf Anwar, University of Engineering and Technology, Lahore, Pakistan  

Copyright: © 2018 Pinti, Scholkmann, Hamilton, Burgess and Tachtsidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Paola Pinti, University College London, London, WC1E 6BT, United Kingdom, p.pinti@ucl.ac.uk