A feature extraction of the EEG using the factor analysis and neural networks

It is often known that an EEG has the personal characteristic. However, there axe 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 CA, 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% accuracy. Then the result, in the case of using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.