Classification of electroencephalogram in listening to the music by multivariate analysis

Takahiro Ogawa, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish
Number of pages5
Publication statusPublished - 2005 Dec 1
Externally publishedYes
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10


OtherSICE Annual Conference 2005


  • Electroencepharogram
  • Multivariate analysis
  • Music therapy
  • Neural network

ASJC Scopus subject areas

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


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