Emotion Recognition and Regulation Based on Stacked Sparse Auto-Encoder Network and Personalized Reconfigurable Music

Abstract

Music can regulate and improve the emotions of the brain. Traditional emotional regulation approaches often adopt complete music. As is well-known, complete music may vary in pitch, volume, and other ups and downs. An individual’s emotions may also adopt multiple states, and music preference varies from person to person. Therefore, traditional music regulation methods have problems, such as long duration, variable emotional states, and poor adaptability. In view of these problems, we use different music processing methods and stacked sparse auto-encoder neural networks to identify and regulate the emotional state of the brain in this paper. We construct a multi-channel EEG sensor network, divide brainwave signals and the corresponding music separately, and build a personalized reconfigurable music-EEG library. The 17 features in the EEG signal are extracted as joint features, and the stacked sparse auto-encoder neural network is used to classify the emotions, in order to establish a music emotion evaluation index. According to the goal of emotional regulation, music fragments are selected from the personalized reconfigurable music-EEG library, then reconstructed and combined for emotional adjustment. The results show that, compared with complete music, the reconfigurable combined music was less time-consuming for emotional regulation (76.29% less), and the number of irrelevant emotional states was reduced by 69.92%. In terms of adaptability to different participants, the reconfigurable music improved the recognition rate of emotional states by 31.32%.