Model of Emotion Judgment Based on Features of Multiple Physiological Signals

Abstract

The model of emotion judgment based on features of multiple physiological signals was investigated. In total, 40 volunteers participated in the experiment by playing a computer game while their physiological signals (skin electricity, electrocardiogram (ECG), pulse wave, and facial electromyogram (EMG)) were acquired. The volunteers were asked to complete an emotion questionnaire where six typical events that appeared in the game were included, and each volunteer rated their own emotion when experiencing the six events. Based on the analysis of game events, the signal data were cut into segments and the emotional trends were classified. The correlation between data segments and emotional trends was built using a statistical method combined with the questionnaire responses. The set of optimal signal features was obtained by processing the data of physiological signals, extracting the features of signal data, reducing the dimensionality of signal features, and classifying the emotion based on the set of signal data. Finally, the model of emotion judgment was established by selecting the features with a significance of 0.01 based on the correlation between the features in the set of optimal signal features and emotional trends.