Applying Meta-Learning and Iso Principle for Development of EEG-Based Emotion Induction System

Music is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016).