1. Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation
- Author
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Chi Wen Jao, Po-Lei Lee, Cheng Hsuan Chen, Kuo Kai Shyu, and Cheng-Kai Lu
- Subjects
hemoglobin response function ,Neurosciences. Biological psychiatry. Neuropsychiatry ,Olfaction ,Article ,03 medical and health sciences ,0302 clinical medicine ,Photoplethysmogram ,machine learning technique ,functional near-infrared spectroscopy ,support vector machine ,Prefrontal cortex ,030304 developmental biology ,Mathematics ,0303 health sciences ,prefrontal cortex ,business.industry ,General Neuroscience ,Pattern recognition ,Support vector machine ,Frontal lobe ,Odor ,classification ,Olfactory stimulation ,Functional near-infrared spectroscopy ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,RC321-571 ,olfaction - 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.
- Published
- 2021