Back
Full-text
Journal article

Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework.

  • Pinti P Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
  • Scholkmann F Department of Neonatology, Biomedical Optics Research Laboratory, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Hamilton A Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
  • Burgess P Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
  • Tachtsidis I Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Show more…
  • 2019-01-29
Published in:
  • Frontiers in human neuroscience. - 2018
digital filter
functional data analysis
functional near infrared spectroscopy
general linear model
pre-processing guidelines
pre-processing standardization
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/26519
Statistics
Document views: 6 File downloads:
  • fulltext.pdf: 0