Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
| Autor: | Han Gao, Rumeng Wu, Hengyang Wang, You Wang, Li Liu, Guang Li, Qing Ai, Dongcheng Lu, Yueling Zhou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Sucrose
Taste Power frequency TP1-1185 Umami Electromyography Sodium Chloride surface electromyography (sEMG) Biochemistry Article Analytical Chemistry Sodium Glutamate Feature (machine learning) medicine Humans Electrical and Electronic Engineering Support Vector Machine (SVM) Instrumentation taste stimuli intensity Mathematics primary taste medicine.diagnostic_test business.industry Chemical technology pattern recognition brain-computer interface (BCI) Pattern recognition Atomic and Molecular Physics and Optics Intensity (physics) Support vector machine Artificial intelligence Feature combination business |
| Zdroj: | Sensors Volume 21 Issue 21 Sensors, Vol 21, Iss 6965, p 6965 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21216965 |
| Popis: | 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 R2 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. |
| Databáze: | OpenAIRE |
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