Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in se...

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Bibliographic Details
Published inAlgorithms Vol. 11; no. 5; p. 67
Main Authors Hocke, Lia M., Oni, Ibukunoluwa K., Duszynski, Chris C., Corrigan, Alex V., Frederick, Blaise deB, Dunn, Jeff F.
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.05.2018
Subjects
channel exclusion
functional Near-Infrared Spectroscopy
GLM
Hemoglobin
LF de-noising
motion correction
Post-processing
pre-processing
single subject
Visual effects
post-processing
channel exclusion
single subject
LF de-noising
functional Near-Infrared Spectroscopy
GLM
pre-processing
motion correction
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Summary:With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.
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Author Contributions: L.M.H. conceived and designed the experiments, analyzed the data and wrote most of the paper; I.K.O. and A.V.C. performed the experiments and I.K.O. helped with the data conversion as well as contributed a figure; C.C.D. wrote the introduction of the paper and contributed to the discussion. J.F.D. and B.deB.F. contributed to analysis and interpretation of the data comparisons and provided funding.
ISSN:1999-4893
1999-4893
DOI:10.3390/a11050067