Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained fr...

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Main Authors: Chen, C.-H., Shyu, K.-K., Lu, C.-K., Jao, C.-W., Lee, P.-L.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29528 /
Published: MDPI AG 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107358101&doi=10.3390%2fbrainsci11060701&partnerID=40&md5=ce986495fbe5024c9d90fdc9e070e201
http://eprints.utp.edu.my/29528/
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spelling utp-eprints.295282022-03-25T02:08:22Z Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation Chen, C.-H. Shyu, K.-K. Lu, C.-K. Jao, C.-W. Lee, P.-L. The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107358101&doi=10.3390%2fbrainsci11060701&partnerID=40&md5=ce986495fbe5024c9d90fdc9e070e201 Chen, C.-H. and Shyu, K.-K. and Lu, C.-K. and Jao, C.-W. and Lee, P.-L. (2021) Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation. Brain Sciences, 11 (6). http://eprints.utp.edu.my/29528/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Chen, C.-H.
Shyu, K.-K.
Lu, C.-K.
Jao, C.-W.
Lee, P.-L.
spellingShingle Chen, C.-H.
Shyu, K.-K.
Lu, C.-K.
Jao, C.-W.
Lee, P.-L.
Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
author_sort Chen, C.-H.
title Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
title_short Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
title_full Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
title_fullStr Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
title_full_unstemmed Classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
title_sort classification of prefrontal cortex activity based on functional near-infrared spectroscopy data upon olfactory stimulation
publisher MDPI AG
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107358101&doi=10.3390%2fbrainsci11060701&partnerID=40&md5=ce986495fbe5024c9d90fdc9e070e201
http://eprints.utp.edu.my/29528/
_version_ 1741197256630992896
score 11.62408

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