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Algorithms 2018, 11(5), 67; https://doi.org/10.3390/a11050067

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

1
Experimental Imaging Lab, Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4Z6, Canada
2
McLean Imaging Center, McLean Hospital, Belmont, MA 02478, USA
3
Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
4
Alberta Children’s Hospital Research Institute, Calgary, AB T3B 6A8, Canada
*
Author to whom correspondence should be addressed.
Received: 6 April 2018 / Revised: 1 May 2018 / Accepted: 2 May 2018 / Published: 8 May 2018
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Abstract

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. View Full-Text
Keywords: functional Near-Infrared Spectroscopy; pre-processing; post-processing; channel exclusion; motion correction; LF de-noising; GLM; single subject functional Near-Infrared Spectroscopy; pre-processing; post-processing; channel exclusion; motion correction; LF de-noising; GLM; single subject
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Hocke, L.M.; Oni, I.K.; Duszynski, C.C.; Corrigan, A.V.; Frederick, B.D.; Dunn, J.F. Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences. Algorithms 2018, 11, 67.

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