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 proceedingConference 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 languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages2823-2826
Number of pages4
DOIs
Publication statusPublished - 2007 Dec 1
Externally publishedYes
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

Keywords

  • Electroencephalogram
  • Genetic algorithms
  • Personal features
  • Visualization

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'The EEG feature extraction method of listening to music using the genetic algorithms and the latency structure model'. Together they form a unique fingerprint.

  • Cite this

    Ito, S. I., Mitsukura, Y., Miyamura, H. N., 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 (pp. 2823-2826). [4421469] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421469