Mind to Music: An EEG Signal-Driven Real-Time Emotional Music Generation System

Abstract

Music is an important way for emotion expression, and traditional manual composition requires a solid knowledge of music theory. It is needed to find a simple but accurate method to express personal emotions in music creation. In this paper, we propose and implement an EEG signal-driven real-time emotional music generation system for generating exclusive emotional music. To achieve real-time emotion recognition, the proposed system can obtain the model suitable for a newcomer quickly through short-time calibration. And then, both the recognized emotion state and music structure features are fed into the network as the conditional inputs to generate exclusive music which is consistent with the user’s real emotional expression. In the real-time emotion recognition module, we propose an optimized style transfer mapping algorithm based on simplified parameter optimization and introduce the strategy of instance selection into the proposed method. The module can obtain and calibrate a suitable model for a new user in short-time, which achieves the purpose of real-time emotion recognition. The accuracies have been improved to 86.78% and 77.68%, and the computing time is just to 7 s and 10 s on the public SEED and self-collected datasets, respectively. In the music generation module, we propose an emotional music generation network based on structure features and embed it into our system, which breaks the limitation of the existing systems by calling third-party software and realizes the controllability of the consistency of generated music with the actual one in emotional expression. The experimental results show that the proposed system can generate fluent, complete, and exclusive music consistent with the user’s real-time emotion recognition results.