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

Shin Ichi Ito, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to journalConference article

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% 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.

Original languageEnglish
Pages (from-to)609-616
Number of pages8
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2773 PART 1
Publication statusPublished - 2003 Dec 1
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 2003 Sep 32003 Sep 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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