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Article

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

1
Department of Electrical Engineering, National Central University, No.300, Zhongda Rd., Zhongli District, Taoyuan City 32001, Taiwan
2
Department of Electrical Engineering, Fu Jen Catholic University, No.510, Zhongzheng Rd., Xinzhuang District, New Taipei City 242062, Taiwan
3
Institute of Health & Analytics for Personalised Care, Universiti Teknologi PETRONAS, Seri Iskander 32610, Perak, Malaysia
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Institute of Biophotonics, National Yang-Ming University, No.155, Sec. 2, Linong Street, Taipei 11221, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Dimiter Prodanov
Brain Sci. 2021, 11(6), 701; https://doi.org/10.3390/brainsci11060701
Received: 11 March 2021 / Revised: 30 April 2021 / Accepted: 19 May 2021 / Published: 26 May 2021
(This article belongs to the Special Issue Neuroinformatics and Signal Processing)
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. View Full-Text
Keywords: functional near-infrared spectroscopy; olfaction; hemoglobin response function; support vector machine; classification; machine learning technique; prefrontal cortex functional near-infrared spectroscopy; olfaction; hemoglobin response function; support vector machine; classification; machine learning technique; prefrontal cortex
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MDPI and ACS Style

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. Brain Sci. 2021, 11, 701. https://doi.org/10.3390/brainsci11060701

AMA Style

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. Brain Sciences. 2021; 11(6):701. https://doi.org/10.3390/brainsci11060701

Chicago/Turabian Style

Chen, Cheng-Hsuan, Kuo-Kai Shyu, Cheng-Kai Lu, Chi-Wen Jao, and Po-Lei Lee. 2021. "Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation" Brain Sciences 11, no. 6: 701. https://doi.org/10.3390/brainsci11060701

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