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. 2020 Feb 18;14:30. doi: 10.3389/fnhum.2020.00030

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Copyright © 2020 von Lühmann, Ortega-Martinez, Boas and Yücel.

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.

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Typical BCI preprocessing pipeline for fNIRS: Linear detrending, pruning of channels with low SNR, artifact rejection, conversion to HbO/HbR with modified Beer-Lambert Law (mBLL), low-pass (LP), or band-pass (BP) filtering. Sequence of blocks can deviate. (Down) established General Linear Model in fNIRS and fMRI Neuroscience using the current best practice: short-separation regression (GLM with SS). We propose to include the GLM into BCI approaches for enhanced performance by using better estimates of the hemodynamic response in fNIRS. Please note that drift removal (detrending) is part of the GLM when polynomial regressors are provided.