Emotion Recognition Based on Representation Dissimilarity Matrix

Emotion recognition based on electroencephalogram (EEG) has been widely concerned because it could reflect intrinsic emotional information. Although a large number of achievements have been made, great challenges still exist. For example, strict identification conditions make it difficult to apply in real life. Therefore, an experimental method of emotion induction based on daily sounds is proposed in this article, which is closer to the everyday work environment. Then, a feature optimization method based on the representation dissimilarity matrix is proposed. Finally, the feature evaluation criteria are established and the emotion-related features are found. In this article, EEG data of 16 volunteers in different emotional sounds were collected. Three types of EEG feature: high-order crossing, power spectral density and difference asymmetry were extracted. After feature optimization, and model construction, the recognition rate of high and low valence was up to 69%. This study explores the dynamic response of people listening to sound and shows that the environmental sound could effectively induce and recognize emotional status, which could better help AI understand people’s preferences and needs.