Abstract—Music can improve human mental and physical states. Thus, music is used in various areas such as sport training and music therapy. However, it is well known that music is affected greatly by human emotions as well as surrounding environment. Therefore, it is important to observe the current human emotion and the environment to increase the effectiveness of music-based sports training or music therapy. This paper examined the practical method to identify human emotion using brainwaves. In order to find the best method for human emotion detection, this paper conducted experiments to acquire brainwaves and determine human mental states. Based on the results of these experiments, the author proposes the optimal condition for classifying mental states using machine learning. The results show that Lγ and Mγ are elements of the important feature vector to improve classification accuracy. Using this method, the author plan to develop an accurate music recommender system, which should be effective for music-based sports training or music therapy can be developed.

Index Terms—music, brainwave, music therapy, sport training, machine learning, recommender system

Cite: Sungri Chong, Ryosuke Yamanishi, Yasuhiro Tsubo, Haruo Sakuma, and Kyoji Kawagoe, "Feature Vectors in Mental-State Classification Using Forehead-mounted Electrical Potential Monitoring Device," Vol. 7, No. 3, pp. 214-219, August, 2016. doi: 10.12720/jait.7.3.214-219