Abstract
Recently, emotion recognition has gained a lot of attentions particularly in the domain of music recommendation since music can induce emotions. There have been many attempts to predict emotions using Electroencephalograph (EEG) signal, a recording of brain electrical activity. However, almost all of them just employed conventional classification techniques. In this paper, we present a novel emotion prediction algorithm by applying an item-based collaborative filtering (CF). A new EEG-based similarity score is invented to be used in the proposed method. Two-dimensional emotion model is employed, and both valence and arousal levels are discretized into two classes. The experiments were conducted on two data sets: benchmark data and our own collected data. The results show that our method does not only outperform the traditional CF, but also existing classification techniques, i.e., C4.5, SVM, and MLP.
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The software is developed for Polymate AP1532 by TEAC Corporation.
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Vateekul, P., Thammasan, N., Moriyama, K., Fukui, Ki., Numao, M. (2016). Item-Based Learning for Music Emotion Prediction Using EEG Data. In: Baldoni, M., et al. Principles and Practice of Multi-Agent Systems. CMNA IWEC IWEC 2015 2015 2014. Lecture Notes in Computer Science(), vol 9935. Springer, Cham. https://doi.org/10.1007/978-3-319-46218-9_13
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DOI: https://doi.org/10.1007/978-3-319-46218-9_13
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