A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state

Shin Ichi Ito, Yasue Mitsukura, Katsuya Sato, Shoichiro Fujisawa, Minoru Fukumi

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

1 Citation (Scopus)

Abstract

This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A K-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.

Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Pages4229-4232
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009 - Porto, Portugal
Duration: 2009 Nov 32009 Nov 5

Other

Other35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009
CountryPortugal
CityPorto
Period09/11/309/11/5

Fingerprint

Electroencephalography
Pattern recognition
Power spectrum
Frequency bands
Classifiers

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Ito, S. I., Mitsukura, Y., Sato, K., Fujisawa, S., & Fukumi, M. (2009). A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state. In IECON Proceedings (Industrial Electronics Conference) (pp. 4229-4232). [5415062] https://doi.org/10.1109/IECON.2009.5415062

A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state. / Ito, Shin Ichi; Mitsukura, Yasue; Sato, Katsuya; Fujisawa, Shoichiro; Fukumi, Minoru.

IECON Proceedings (Industrial Electronics Conference). 2009. p. 4229-4232 5415062.

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

Ito, SI, Mitsukura, Y, Sato, K, Fujisawa, S & Fukumi, M 2009, A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state. in IECON Proceedings (Industrial Electronics Conference)., 5415062, pp. 4229-4232, 35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009, Porto, Portugal, 09/11/3. https://doi.org/10.1109/IECON.2009.5415062
Ito SI, Mitsukura Y, Sato K, Fujisawa S, Fukumi M. A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state. In IECON Proceedings (Industrial Electronics Conference). 2009. p. 4229-4232. 5415062 https://doi.org/10.1109/IECON.2009.5415062
Ito, Shin Ichi ; Mitsukura, Yasue ; Sato, Katsuya ; Fujisawa, Shoichiro ; Fukumi, Minoru. / A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state. IECON Proceedings (Industrial Electronics Conference). 2009. pp. 4229-4232
@inproceedings{a4e4ccba86334e68a1df23fb3d76d565,
title = "A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state",
abstract = "This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A K-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.",
author = "Ito, {Shin Ichi} and Yasue Mitsukura and Katsuya Sato and Shoichiro Fujisawa and Minoru Fukumi",
year = "2009",
doi = "10.1109/IECON.2009.5415062",
language = "English",
pages = "4229--4232",
booktitle = "IECON Proceedings (Industrial Electronics Conference)",

}

TY - GEN

T1 - A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state

AU - Ito, Shin Ichi

AU - Mitsukura, Yasue

AU - Sato, Katsuya

AU - Fujisawa, Shoichiro

AU - Fukumi, Minoru

PY - 2009

Y1 - 2009

N2 - This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A K-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.

AB - This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A K-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.

UR - http://www.scopus.com/inward/record.url?scp=77951547499&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951547499&partnerID=8YFLogxK

U2 - 10.1109/IECON.2009.5415062

DO - 10.1109/IECON.2009.5415062

M3 - Conference contribution

AN - SCOPUS:77951547499

SP - 4229

EP - 4232

BT - IECON Proceedings (Industrial Electronics Conference)

ER -