LSTM based Reinforcement Learning for Electroencephalogram Music Generation

EEG signals represent the activity of neurons in the brain, containing complex information that can effectively reflect human’s physiological state. Music stimulation acts on the brain, causing corresponding changes in EEG signals, thus there is an inseparable connection between EEG signals and music. This article studies the neural control of the brain by using EEG signals to generate music. This article uses EEG as input, designs a new reward function using LSTM and music theory rules, and uses this reward function to design a music generation method based on Q-Learning. Through experiments, it has been shown that the method proposed in this article can generate music with ornamental properties that have a similar distribution to real music.