The proposal of the EEG characteristics extraction method in weighted principal frequency components using the RGA

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components are different. The real-coded genetic algorithm (: RGA) is used for selecting and being weighted the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of only one measurement point. In order to show the effectiveness of the proposed method, computer simulations are carried out by using real data.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Pages1152-1155
Number of pages4
DOIs
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference

Other

Other2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period06/10/1806/10/21

Keywords

  • Electroencephalogram
  • Latency structure model
  • Mental change
  • Real-coded genetic algorithms (RGA)

ASJC Scopus subject areas

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

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