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Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints

Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an in...

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Autores principales: Ren, Haonan, Zhou, Shufeng, Zhang, Limei, Zhao, Feng, Qiao, Lishan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873366/
https://www.ncbi.nlm.nih.gov/pubmed/35221902
http://dx.doi.org/10.3389/fnins.2022.813293
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author Ren, Haonan
Zhou, Shufeng
Zhang, Limei
Zhao, Feng
Qiao, Lishan
author_facet Ren, Haonan
Zhou, Shufeng
Zhang, Limei
Zhao, Feng
Qiao, Lishan
author_sort Ren, Haonan
collection PubMed
description Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging, electroencephalogram, or magnetoencephalography data. Functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities; in this paper, we investigate whether fNIRS-based BFN could be used as a “fingerprint” to identify individuals. In particular, Pearson's correlation is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: (1) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual.
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spelling pubmed-88733662022-02-26 Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints Ren, Haonan Zhou, Shufeng Zhang, Limei Zhao, Feng Qiao, Lishan Front Neurosci Neuroscience Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging, electroencephalogram, or magnetoencephalography data. Functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities; in this paper, we investigate whether fNIRS-based BFN could be used as a “fingerprint” to identify individuals. In particular, Pearson's correlation is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: (1) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual. Frontiers Media S.A. 2022-02-11 /pmc/articles/PMC8873366/ /pubmed/35221902 http://dx.doi.org/10.3389/fnins.2022.813293 Text en Copyright © 2022 Ren, Zhou, Zhang, Zhao and Qiao. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Neuroscience
Ren, Haonan
Zhou, Shufeng
Zhang, Limei
Zhao, Feng
Qiao, Lishan
Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title_full Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title_fullStr Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title_full_unstemmed Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title_short Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints
title_sort identifying individuals by fnirs-based brain functional network fingerprints
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873366/
https://www.ncbi.nlm.nih.gov/pubmed/35221902
http://dx.doi.org/10.3389/fnins.2022.813293
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