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

Abstract

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.

fNIRS neuroimaging in olfactory research: A systematic literature review

“…The use of multichannel configurations in almost all studies can confirm that the presentation of an odor elicits a change in activity, most often an increase in HbO2 concentration, in specific regions such as the orbitofrontal cortex. This is in line with the understanding that the orbital cortex receives direct axonal connections from the primary olfactory cortex (Chen et al, 2021).…”

“…Devices used included the NIRSport NIRx device (Gramlich et al, 2017 ), the Pocket NIRS Duo, DynaSense, by Hamamatsu (Kunihiro et al, 2017 ), the Hb133 by Astem Co. (Yamashita et al, 2022 ), and the HOT-1000, WOT-100 by Neu Systems Inc (Rajagoplan, 2018 ; Tomono et al, 2021 ). The recent development of in-house custom systems for odor research has also shown a tendency toward low channel density, as in the case of Moein et al ( 2020 ) and Chen et al ( 2021 ). The need to combine fNIRS with other wearable systems, particularly virtual reality headsets, may also contribute to lower channel numbers (Kaimal et al, 2021 ).…”

“…Three studies opted for olfactometer odor delivery (Kokan et al, 2011 ; Hucke et al, 2018 ; Invitto et al, 2019 ). Most studies opted for odor containers, such as bottles (Saito-Iizumi et al, 2013 ; Maharjan et al, 2019 ; Moein et al, 2020 ; Chen et al, 2021 ; Yamashita et al, 2022 ), test tubes (Fladby et al, 2004 ), odor filled bags (Igarashi et al, 2014 ; Ikei et al, 2015 ), climate controlled room ( Figures 11a , b ) (Song et al, 2019 ) and pressure containers ( Figure 11c ) (Jo et al, 2013 ). Jo et al ( 2013 ) incorporated naturally found stimuli, e.g.…”

“…The most common fNIRS odor paradigm involved passive inhalation whilst in a seated and comfortable position (Bartocci et al, 2000 , 2001 ; Fladby et al, 2004 ; Ishimaru et al, 2004 ; Harada et al, 2006 ; Kobayashi et al, 2009 ; Aoyama et al, 2010 ; Frie et al, 2017 , 2019 ; Hucke et al, 2018 ; Casillas-Figueroa et al, 2020 ; Ho et al, 2021 ). Some studies also utilized questionnaires during or after the fNIRS recording phase to measure odor stimuli hedonic value and intensity for each odor presented (Kokan et al, 2011 ; Jo et al, 2013 ; Nishitani et al, 2014 ; Azuma et al, 2016 ; Gramlich et al, 2017 ; Kunihiro et al, 2017 ; Chen et al, 2021 ; Tomono et al, 2021 ). A few studies implemented tasks requiring the participant to identify the moment of odor detection and recognition during testing, either verbally, or using a button (Kokan et al, 2011 ; Moein et al, 2020 ; Xu et al, 2020 ).…”

Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction

“…By using the algorithm proposed in this study, it is possible to identify heartbeat signals more clearly with fNIRS and help track cognitive function, stress levels, and age-related changes [30]. This can be used as auxiliary indicators that can be referenced physiologically and neurologically in the clinical environment and provide a more insightful approach in the development of a big data platform.…”