Abstract
In order to solve a stress problem, researchers have studied music therapy. It takes the therapist and patient a long time to select the music. Because the music used in music therapy is of various type. If the music for it is easily selectable, the music therapy can be carried out more effectively. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. In this paper, we propose a method that extracts features of the EEG by PCA (principal component analysis) and CDA (canonical discriminant analysis). Then we analyze each feature data by NN (neural network). In order to examine whether the proposal system is effective, we try computer simulations for the EEG classification. According to recognition rate by the NN, it was considered that the CDA extracted and classified the features of the EEG better than the PCA.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ogawa, T., Ota, S., Ito, Si., Mitsukura, Y., Fukumi, M., Akamatsu, N. (2005). Influence of Music Listening on the Cerebral Activity by Analyzing EEG. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_94
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DOI: https://doi.org/10.1007/11552413_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
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