Reinforcement Learning using EEG signals for Therapeutic Use of Music in Emotion Management.

Prolonged influence of negative emotions can result in clinical depression or anxiety, and while many prescribed techniques exist, music therapy approaches, coupled with psychotherapy, have shown to help lower depressive symptoms, supplementing traditional treatment approaches. Identifying the appropriate choice of music, therefore, is of utmost importance. Selecting appropriate playlists, however, are challenged by user feedback that may inadvertently select songs that amplify the negative effects. Therefore, this work uses electroencephalogram (EEG) that automatically identifies the emotional impact of music and trains a reinforcement-learning approach to identify an adaptive personalized playlist of music to lead to improved emotional states. This work uses data from 32 users, collected in the publicly available DEAP dataset, to select songs for users that guide them towards joyful emotional states. Using a domain-specific reward-shaping function, a Q-learning agent is able to correctly guide a majority of users to the target emotional states, represented in a common emotion wheel. The average angular error of all users is 57°, with a standard deviation of 2.8 and the target emotional state is achieved.Clinical relevance- Music therapy for improving clinical depression and anxiety can be supplemented by additional emotion-guided music decisions in remote and personal settings by using automated techniques to capture emotional state and identify music that best guides users to target joyful states.