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

doi:10.1109/SICE.2006.315293

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 proceeding › Conference contribution

3 Citations (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 language	English
Title of host publication	2006 SICE-ICASE International Joint Conference
Pages	1152-1155
Number of pages	4
DOIs	https://doi.org/10.1109/SICE.2006.315293
Publication status	Published - 2006 Dec 1
Externally published	Yes
Event	2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of Duration: 2006 Oct 18 → 2006 Oct 21

Publication series

Name	2006 SICE-ICASE International Joint Conference

Other

Other	2006 SICE-ICASE International Joint Conference
Country/Territory	Korea, Republic of
City	Busan
Period	06/10/18 → 06/10/21

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

Access to Document

10.1109/SICE.2006.315293

Cite this

The proposal of the EEG characteristics extraction method in weighted principal frequency components using the RGA. / Ito, Shin Ichi; Mitsukura, Yasue; Miyamura, Hiroko Nakamura et al.

2006 SICE-ICASE International Joint Conference. 2006. p. 1152-1155 4109136 (2006 SICE-ICASE International Joint Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ito, SI, Mitsukura, Y, Miyamura, HN, Saito, T & Fukumi, M 2006, The proposal of the EEG characteristics extraction method in weighted principal frequency components using the RGA. in 2006 SICE-ICASE International Joint Conference., 4109136, 2006 SICE-ICASE International Joint Conference, pp. 1152-1155, 2006 SICE-ICASE International Joint Conference, Busan, Korea, Republic of, 06/10/18. https://doi.org/10.1109/SICE.2006.315293

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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.",

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AU - Ito, Shin Ichi

AU - Mitsukura, Yasue

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AU - Fukumi, Minoru

PY - 2006/12/1

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AB - 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.

KW - Electroencephalogram

KW - Latency structure model

KW - Mental change

KW - Real-coded genetic algorithms (RGA)

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

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Keywords

ASJC Scopus subject areas

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