The EEG feature extraction using the principal component analysis

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

Research output: Contribution to conferencePaperpeer-review


The diseases based on the stress are increasing in recent years. As one of the way deal with these diseases, music therapy is used. An effective on the music for music therapy is depended on a person. Therefore, it is important to select the music suitable for a person. In this paper, a system automatically selects the suitable music for music therapy from a large database of music by each EEG. By using this system, music therapy is done regardless of time and place, and it is needed for constructing this system to clear relations between the music and the EEG. In this paper, we measure the EEG of subjects in listening to music and extract some features of their patterns by using the Principal Component Analysis (PCA). Then we analyze them by using the neural networks (NN). Finally, in order to demonstrate the effective of the proposed method, we carry out the computer simulation. Then, we show the effectiveness of the proposed method.

Original languageEnglish
Number of pages4
Publication statusPublished - 2005
Externally publishedYes
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10


OtherSICE Annual Conference 2005


  • EEG
  • Music therapy
  • NN
  • PCA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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