Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences - Hocke, Lia M.; Oni, Ibukunoluwa K.; Duszynski, Chris C.; Corrigan, Alex V.; Frederick, Blaise deB; Dunn, Jeff F. (Algorithms, 2018)
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.