Currently, how to equip machines with the ability for properly recognizing users' felt-emotion during multimedia presentation is a growing issue. In this study we focused on the approach for recognizing music-induced emotional responses from brain activity. A comparative study was conducted to testify the feasibility of using hierarchical binary classifiers to improve the classification performance as compared with nonhierarchical schemes. According to our classification results, we not only found that using one-against-one scheme of hierarchical binary classifier results in an improvement to performance, but also established an alternative solution for emotion recognition by proposed model-based scheme depending on 2D emotion model.
['Yuan-Pin Lin', 'Chi-Hong Wang', 'Tien-Lin Wu', 'Shyh-Kang Jeng', 'Jyh-Horng Chen']
EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine