A Music Recommendation System for Depression Therapy Based on EEG

Depression is a civilized disease that commonly exists in human society. Music therapy can help relieve emotional and psychological stress. How to monitor and capture human psychological conditions can be understood through brainwave signals. With the advancement of technology and the spread of communication technology, wearable devices are becoming more comfortable and applied to people's lives. This paper will use a commercially available EEG Bluetooth headset that can capture brain wave changes (such as alpha and beta wave) through sensors. Through the Bluetooth transmission function, data can be transmitted securely with the mobile device. The EEG signal can provide a wide range of information about various cognitive dysfunctions and diseases. Further analysis of the EEG signal, classification and classification of depression level, in addition to providing medical staff as a reference for treatment, can also be used as an evaluation basis for music therapy. Therefore, this paper will implement a music recommendation system to provide users with a list of soothing emotions according to different symptoms and degrees. In music analysis, this paper will collect a variety of different types of music, through field verification and algorithm combination, to achieve a more robust depression music therapy recommendation system.