Posterior probability estimation for actual and artifactual components from MEG data

Montn Phothisonothai, Katsumi Watanabe, Yuko Yoshimura

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

Abstract

The presence of physiological artifacts from magnetoencephalogram (MEG) data, e.g., eye movements, muscular contractions, cardiac signals, sudden high-amplitude changes, and environmental noise reduce the correctness of interpretation. Therefore, the automatic artifact removal is needed. In this paper, we present a posterior probabilities of actual and artifactual components in order to determine optimal threshold values for each parameter. The results showed that the actual and artifactual MEG components were classified clearly by using optimal threshold values of 1.352, 0.017, 0.443, 0.949, and 0.963 for kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD), respectively.

Original languageEnglish
Title of host publicationProceedings of the 2013 5th International Conference on Knowledge and Smart Technology, KST 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages176-177
Number of pages2
ISBN (Electronic)9781467348508
DOIs
Publication statusPublished - 2013
Event5th International Conference on Knowledge and Smart Technology, KST 2013 - Chonburi, Thailand
Duration: 2013 Jan 312013 Feb 1

Publication series

NameProceedings of the 2013 5th International Conference on Knowledge and Smart Technology, KST 2013

Conference

Conference5th International Conference on Knowledge and Smart Technology, KST 2013
Country/TerritoryThailand
CityChonburi
Period13/1/3113/2/1

Keywords

  • (Magnetoencephalography)
  • Cognitive neurosicence
  • Duration information
  • MEG
  • Short-term memory

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

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