Sweet and sour taste classification using EEG based brain computer interface

Abidi, Ismi; Farooq, Omar; Beg, M. M. Sufyan

doi:10.1109/indicon.2015.7443230

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some progress has been made based on the EEG. For instance, Abidi used an EEG for assessing sweetness and sourness and got an accuracy of 98% [ 23 ]; Andersen analyzed EEG differences under the stimuli of caloric sucrose, low-caloric aspartame, and a low-caloric mixture of aspartame and acesulfame K [ 24 ]. However, the EEG has the problem of excessive channels, information redundancy, signals capturing complexity, and data processing difficulty [ 10 , 11 ], while a surface electromyography (sEMG) system could solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.

show abstract

Section: Introductionmentioning

confidence: 99%

Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…However, these physiological signals were limited by the requirements for the site and large equipment. Among many physiological signals in BCI applications, electroencephalogram (EEG) is popular for its non-invasiveness, information integrity, and device portability [19][20][21] and has been applied for the recognition of taste sensations [22][23][24][25]. However, EEG also has shortcomings [26], such as irrelevant channels, overwhelming information, and the complexity of signal acquisition and processing, which could be solved by surface electromyography (sEMG), benefitting by pasting electrodes specifically on the related muscles and avoiding the influence of skull and hair.…”

Section: Introductionmentioning

confidence: 99%

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

Wang

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract