Causality analysis of multichannel alpha activities using multidimensional directed information

Osamu Sakata, Tsuyoshi Shiina, Yoichi Saito

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

Abstract

Analysis of information flow pattern among different portions on the scalp is important for investigating the relation between EEG and mental activity. Therefore, far the causality analysis, that is, the detection of the propagation path and direction of each component of EEG among different portions on the scalp, we have proposed a method based on the multidimensional directed information. The performance of the method was evaluated by simulation using signal generation models. Moreover, the method was applied to the analysis of alpha activities recorded for normal subjects and a patient whose brain has organic disorder. The result showed that the detected pattern of information flow was well correlated with the disordered portion, consequently the method was a promising way for the estimation of mental activities.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
Pages433
Number of pages1
ISBN (Print)0780356756
Publication statusPublished - 1999
Externally publishedYes
EventProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA
Duration: 1999 Oct 131999 Oct 16

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
ISSN (Print)0589-1019

Other

OtherProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
CityAtlanta, GA, USA
Period99/10/1399/10/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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