A wearable system using single-channel EEG, respiration, ECG, and body postures with support vector machine classification extracted key features for emotion classification with reduced algorithm complexity.
A wearable system using multiple physiological signals and SVM classification can effectively classify human emotions with reduced algorithm complexity compared to traditional multi-channel EEG.
BACKGROUND: Human emotion classification is traditionally achieved using multi-channel electroencephalogram (EEG) signal, which requires costly equipment and complex classification algorithms. OBJECTIVE: The experiments can be implemented in the laboratory environment equipped with high-performance computers for the online analysis; this will hinder the usability in practical applications. METHODS: Considering that other physiological signals are also associated with emotional changes, this paper proposes to use a wearable, wireless system to acquire a single-channel electroencephalogram signal, respiration, electrocardiogram (ECG) signal, and body postures to explore the relationship between these signals and the human emotions. RESULTS AND CONCLUSIONS: Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification. The proposed method can support an embedded on-line analysis and may enhance the usability of emotion classification.
Liu et al. (Mon,) conducted a other in Human emotion classification. Wearable wireless system (single-channel EEG, respiration, ECG, body postures) with SVM classification vs. Traditional emotion classification method (multi-channel EEG) was evaluated on Emotion classification performance and algorithm complexity. A wearable system using single-channel EEG, respiration, ECG, and body postures with support vector machine classification extracted key features for emotion classification with reduced algorithm complexity.