Abstract
It is often known that an EEG has the personal characteristic. However, there are no researches to achieve the considering of the personal characteristic. Then, the analyzed frequency components of the EEG have that the frequency components in which characteristics are contained significantly, and that not. Moreover, these combinations have the human equation. We think that these combinations are the personal characteristics frequency components of the EEG. In this paper, the EEG analysis method by using the GA, the FA, and the NN is proposed. The GA is used for selecting the personal characteristics frequency compnents. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern does computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The result, in the case of not using the personal characteristics frequency components, gave over 80 \{ using the personal characteristics frequency components, gave over 95 \{ effectiveness of the proposed method.
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© 2003 Springer-Verlag Berlin Heidelberg
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Ito, Si., Mitsukura, Y., Fukumi, M., Akamatsu, N. (2003). A Feature Extraction of the EEG Using the Factor Analysis and Neural Networks. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_83
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DOI: https://doi.org/10.1007/978-3-540-45224-9_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
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