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dc.contributor.authorvon Lühmann, Alexanderen_US
dc.contributor.authorOrtega-Martinez, Antonioen_US
dc.contributor.authorBoas, David A.en_US
dc.contributor.authorYücel, Meryem Ayşeen_US
dc.coverage.spatialSwitzerlanden_US
dc.date2020-01-22
dc.date.accessioned2020-05-14T19:54:40Z
dc.date.available2020-05-14T19:54:40Z
dc.date.issued2020
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/32132909
dc.identifier.citationvon Lühmann A, Ortega-Martinez A, Boas DA and Yücel MA (2020) Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Front. Hum. Neurosci. 14:30. https://doi.org/10.3389/fnhum.2020.00030
dc.identifier.issn1662-5161
dc.identifier.urihttps://hdl.handle.net/2144/40889
dc.description.abstractWithin a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.en_US
dc.format.extent17 pagesen_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofFront Hum Neurosci
dc.rightsCopyright © 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.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBCIen_US
dc.subjectGLMen_US
dc.subjectHRFen_US
dc.subjectClassificationen_US
dc.subjectfNIRSen_US
dc.subjectNuisance regressionen_US
dc.subjectPreprocessingen_US
dc.subjectShort-separationen_US
dc.subjectExperimental psychologyen_US
dc.subjectNeurosciencesen_US
dc.subjectPsychologyen_US
dc.subjectCognitive sciencesen_US
dc.titleUsing the general linear model to improve performance in fNIRS single trial analysis and classification: a perspectiveen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3389/fnhum.2020.00030
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Biomedical Engineeringen_US
pubs.publication-statusPublished onlineen_US
dc.identifier.orcid0000-0002-6709-7711 (Boas, David A)
dc.identifier.mycv552785


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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.
Except where otherwise noted, this item's license is described as 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.