A basic method for classifying humans based on an EEG analysis

Shin ichi Ito, Yasue Mitsukura, Minoru Fukumi

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

3 Citations (Scopus)

Abstract

This paper introduces a method for classifying humans by analyzing prefrontal cortex electroencephalogram (EEG) activity to extract and confirm distinct response features on listening to music that the user feels matches his/her mood, does not match his/her mood, or otherwise. The proposed method constitutes analyzing EEG signals obtained from monitoring human response features and classifying the human subjects according to the different frequency bands of the power spectrum of the EEG signal. The performance of the proposed method is evaluated using real EEG data. We confirm that we can classify humans into at least three groups.

Original languageEnglish
Title of host publication2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Pages1783-1786
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, Viet Nam
Duration: 2008 Dec 172008 Dec 20

Publication series

Name2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Other

Other2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
CountryViet Nam
CityHanoi
Period08/12/1708/12/20

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Keywords

  • Electroencephalogram
  • Human feature
  • Matching mood
  • Music

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Ito, S. I., Mitsukura, Y., & Fukumi, M. (2008). A basic method for classifying humans based on an EEG analysis. In 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 (pp. 1783-1786). [4795798] (2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008). https://doi.org/10.1109/ICARCV.2008.4795798