The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model

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

doi:10.1109/SICE.2007.4421469

The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model

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

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

1 Citation (Scopus)

Abstract

It is known that an electroencephalogram (EEG) is characterized by the unique and personal features of an individual. The EEG frequency components are contained the significant and immaterial information, and then each importance of these frequency components is different. These combinations are often unique like individual human beings and yet they have underlying basic characteristics. We think that these combinations and/or the importance of the frequency components show the personal features. Therefore we propose the two techniques for estimating the personal features. A simple genetic algorithm is used for specifying these frequency combinations. Other technique, a real-coded genetic algorithm is used for estimating the importance of EEG frequency components. Then a latency structure model based on the personal features is used for extracted the feature vector of the EEG. Furthermore, the visualization map is used for evaluating the extracted feature vector of the EEG. In order to show the effectiveness of the proposed methods, the performance of the proposed method is evaluated using real EEG data.

Original language	English
Title of host publication	SICE Annual Conference, SICE 2007
Pages	2823-2826
Number of pages	4
DOIs	https://doi.org/10.1109/SICE.2007.4421469
Publication status	Published - 2007 Dec 1
Externally published	Yes
Event	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: 2007 Sept 17 → 2007 Sept 20

Publication series

Name	Proceedings of the SICE Annual Conference

Other

Other	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
Country/Territory	Japan
City	Takamatsu
Period	07/9/17 → 07/9/20

Keywords

Electroencephalogram
Genetic algorithms
Personal features
Visualization

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2007.4421469

Cite this

The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model. / Ito, Shin Ichi; Mitsukura, Yasue; Miyamura, Hiroko Nakamura et al.
SICE Annual Conference, SICE 2007. 2007. p. 2823-2826 4421469 (Proceedings of the SICE Annual Conference).

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

Ito, SI, Mitsukura, Y, Miyamura, HN, Saito, T & Fukumi, M 2007, The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model. in SICE Annual Conference, SICE 2007., 4421469, Proceedings of the SICE Annual Conference, pp. 2823-2826, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 07/9/17. https://doi.org/10.1109/SICE.2007.4421469

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AU - Mitsukura, Yasue

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AU - Fukumi, Minoru

PY - 2007/12/1

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AB - It is known that an electroencephalogram (EEG) is characterized by the unique and personal features of an individual. The EEG frequency components are contained the significant and immaterial information, and then each importance of these frequency components is different. These combinations are often unique like individual human beings and yet they have underlying basic characteristics. We think that these combinations and/or the importance of the frequency components show the personal features. Therefore we propose the two techniques for estimating the personal features. A simple genetic algorithm is used for specifying these frequency combinations. Other technique, a real-coded genetic algorithm is used for estimating the importance of EEG frequency components. Then a latency structure model based on the personal features is used for extracted the feature vector of the EEG. Furthermore, the visualization map is used for evaluating the extracted feature vector of the EEG. In order to show the effectiveness of the proposed methods, the performance of the proposed method is evaluated using real EEG data.

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The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model

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Keywords

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