Estimation system for human-interest degree while watching TV commercials using EEG

Yuna Negishi, Zhang Dou, Yasue Mitsukura

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

2 Citations (Scopus)

Abstract

In this paper, we propose an estimation system for the human-interest degree while watching TV commercials using the electroencephalogram(EEG). When we use this system, we can estimate the human-interest degree easily, sequentially, and simply. In particular, we measure the EEG using a simple electroencephalograph and survey the human-interest degree using questionnaires in a scale. For construction this estimation system, we investigate the relationship between the EEG and the result of questionnaires. In order to evaluate our estimation system, we show results of experiments using real TV commercials.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages46-53
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2011 Nov 28
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 2011 Nov 132011 Nov 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7062 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Neural Information Processing, ICONIP 2011
CountryChina
CityShanghai
Period11/11/1311/11/17

    Fingerprint

Keywords

  • TV commercial
  • electroencephalogram(EEG)
  • human-interest

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Negishi, Y., Dou, Z., & Mitsukura, Y. (2011). Estimation system for human-interest degree while watching TV commercials using EEG. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings (PART 1 ed., pp. 46-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7062 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-24955-6_6