Feature Extraction and Feature Combination for Basic Taste Sensation Recognition Based on Facial Electromyography

Abstract

Taste sensation recognition is valuable for developing virtual taste and the early diagnosis of taste system disorders. Electromyography (EMG) can characterize the information of taste sensation and have the advantages of a high signal-to-noise ratio and easy acquisition. This paper presents feature extraction and feature combination methods for taste sensation knowledge based on facial EMG. The time domain features, frequency domain features, time-frequency domain features, and entropy features are extracted as individual feature sets after data pre-processing. Combinations of different features are performed to acquire multivariate feature representations. Machine learning methods are used to predict taste sensation categories. The combination of the frequency domain, time-frequency domain, and entropy features as feature set and Random Forest method as classifier obtain the highest recognition accuracy. The results provide a reference for feature extraction and selection for basic taste sensation recognition.