Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163047
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dc.contributor.authorBizzego, Andreaen_US
dc.contributor.authorNeoh, Michelleen_US
dc.contributor.authorGabrieli, Giulioen_US
dc.contributor.authorEsposito, Gianlucaen_US
dc.date.accessioned2022-11-22T08:06:55Z-
dc.date.available2022-11-22T08:06:55Z-
dc.date.issued2022-
dc.identifier.citationBizzego, A., Neoh, M., Gabrieli, G. & Esposito, G. (2022). A machine learning perspective on fNIRS signal quality control approaches. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 30, 2292-2300. https://dx.doi.org/10.1109/TNSRE.2022.3198110en_US
dc.identifier.issn1534-4320en_US
dc.identifier.urihttps://hdl.handle.net/10356/163047-
dc.description.abstractDespite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing procedure, Signal Quality Control (SQC) is critical to prevent errors and unreliable results. In fNIRS analysis, SQC currently relies on applying empirical thresholds to handcrafted Signal Quality Indicators (SQIs). In this study, we use a dataset of fNIRS signals (N = 1,340) recorded from 67 subjects, and manually label the signal quality of a subset of segments (N = 548) to investigate the pitfalls of current practices while exploring the opportunities provided by Deep Learning approaches. We show that SQIs statistically discriminate signals with bad quality, but the identification by means of empirical thresholds lacks sensitivity. Alternatively to manual thresholding, conventional machine learning models based on the SQIs have been proven more accurate, with end-to-end approaches, based on Convolutional Neural Networks, capable of further improving the performance. The proposed approach, based on machine learning, represents a more objective SQC for fNIRS and moves towards the use of fully automated and standardized procedures.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_US
dc.rights© The authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.subjectSocial sciences::Psychologyen_US
dc.titleA machine learning perspective on fNIRS signal quality control approachesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen_US
dc.contributor.departmentDivision of Psychologyen_US
dc.identifier.doi10.1109/TNSRE.2022.3198110-
dc.description.versionPublished versionen_US
dc.identifier.volume30en_US
dc.identifier.spage2292en_US
dc.identifier.epage2300en_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsfNIRSen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsSignal Quality Controlen_US
dc.description.acknowledgementThis work was supported in part by the Italian Ministry of University and Research through the Excellence Department Grant Awarded to the Department of Psychology and Cognitive Science, University of Trento, Italy, and in part by the European Union–FSE-REACT-EU, PON Research and Innovation 2014–2020 under Grant DM1062/2021.en_US
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