EEC characteristic extraction method of listening music and objective estimation method based on latency structure model in individual characteristics

Shin Ichi Ito, Yasue Mitsukura, Hiroko Nakamura Miyamura, Takafumi Saito, Minoru Fukumi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real E]EG data. The result of our experiment shows the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)874-881+8
JournalIEEJ Transactions on Electronics, Information and Systems
Volume127
Issue number6
Publication statusPublished - 2007 Jan 1
Externally publishedYes

Keywords

  • Electroencephalogram
  • Individual characteristics
  • Latency structure model
  • Real-coded genetic algorithms
  • Visualization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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