期刊
SENSORS
卷 21, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/s21154994
关键词
primary tastes; taste sensation recognition; random forest; brain-computer interface (BCI); surface electromyography (sEMG)
资金
- Science Foundation of Chinese Aerospace Industry [JCKY2018204B053]
- Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China [ICT2021A13]
A novel recognition method based on sEMG signals was developed to distinguish six primary taste sensations in humans with an accuracy of 74.46%. Optimization of feature combination, electrode positions selection, and analysis of subject diversity can further improve the performance of the model.
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.
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