Artifactual component classification from MEG data using support vector machine

Montri Phothisonothai, Fang Duan, Hiroyuki Tsubomi, Aki Kondo, Kazuyuki Aihara, Yuko Yoshimura, Mitsuru Kikuchi, Yoshio Minabe, Katsumi Watanabe

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

5 Citations (Scopus)

Abstract

Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.

Original languageEnglish
Title of host publication5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
DOIs
Publication statusPublished - 2012 Dec 1
Event5th 2012 Biomedical Engineering International Conference, BMEiCON 2012 - Muang, Ubon Ratchathani, Thailand
Duration: 2012 Dec 52012 Dec 7

Publication series

Name5th 2012 Biomedical Engineering International Conference, BMEiCON 2012

Other

Other5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
CountryThailand
CityMuang, Ubon Ratchathani
Period12/12/512/12/7

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Keywords

  • Artifacts
  • Independent component analysis
  • MEG
  • Magnetoencephalogram
  • Support vector machine

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Phothisonothai, M., Duan, F., Tsubomi, H., Kondo, A., Aihara, K., Yoshimura, Y., Kikuchi, M., Minabe, Y., & Watanabe, K. (2012). Artifactual component classification from MEG data using support vector machine. In 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012 [6465462] (5th 2012 Biomedical Engineering International Conference, BMEiCON 2012). https://doi.org/10.1109/BMEiCon.2012.6465462