Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine...

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Autores principales:	Hengyang Wang, Dongcheng Lu, Li Liu, Han Gao, Rumeng Wu, Yueling Zhou, Qing Ai, You Wang, Guang Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	primary taste taste stimuli intensity pattern recognition Support Vector Machine (SVM) brain-computer interface (BCI) surface electromyography (sEMG) Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/321c7c9cab164464bec1a6f9f1f1b057
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spelling	oai:doaj.org-article:321c7c9cab164464bec1a6f9f1f1b0572021-11-11T19:00:53ZQuantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography10.3390/s212169651424-8220https://doaj.org/article/321c7c9cab164464bec1a6f9f1f1b0572021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6965https://doaj.org/toc/1424-8220A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached <i>R</i>2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.Hengyang WangDongcheng LuLi LiuHan GaoRumeng WuYueling ZhouQing AiYou WangGuang LiMDPI AGarticleprimary tastetaste stimuli intensitypattern recognitionSupport Vector Machine (SVM)brain-computer interface (BCI)surface electromyography (sEMG)Chemical technologyTP1-1185ENSensors, Vol 21, Iss 6965, p 6965 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	primary taste taste stimuli intensity pattern recognition Support Vector Machine (SVM) brain-computer interface (BCI) surface electromyography (sEMG) Chemical technology TP1-1185
spellingShingle	primary taste taste stimuli intensity pattern recognition Support Vector Machine (SVM) brain-computer interface (BCI) surface electromyography (sEMG) Chemical technology TP1-1185 Hengyang Wang Dongcheng Lu Li Liu Han Gao Rumeng Wu Yueling Zhou Qing Ai You Wang Guang Li Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
description	A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached <i>R</i>2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.
format	article
author	Hengyang Wang Dongcheng Lu Li Liu Han Gao Rumeng Wu Yueling Zhou Qing Ai You Wang Guang Li
author_facet	Hengyang Wang Dongcheng Lu Li Liu Han Gao Rumeng Wu Yueling Zhou Qing Ai You Wang Guang Li
author_sort	Hengyang Wang
title	Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_short	Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_full	Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_fullStr	Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_full_unstemmed	Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_sort	quantitatively recognizing stimuli intensity of primary taste based on surface electromyography
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/321c7c9cab164464bec1a6f9f1f1b057
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_version_	1718431634780323840

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

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