Name
Title | ||
|---|---|---|
Reinforcement Learning Using Eeg Signals For Therapeutic Use Of Music In Emotion Management |
Abstract | ||
|---|---|---|
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 degrees, with a standard deviation of 2.8 and the target emotional state is achieved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9175586 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esha Dutta | 1 | 0 | 0.34 |
| Ananya Bothra | 2 | 0 | 0.34 |
| Theodora Chaspari | 3 | 38 | 19.43 |
| Thomas Ioerger | 4 | 0 | 0.34 |
| Bobak J Mortazavi | 5 | 0 | 0.34 |