Linear and nonlinear features for automatic artifacts removal from MEG data based on ICA

Montri Phothisonothai*, Hiroyuki Tsubomi, Aki Kondo, Mitsuru Kikuchi, Yuko Yoshimura, Yoshio Minabe, Kastumi Watanabe

*この研究の対応する著者

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper presents an automatic method to remove physiological artifacts from magnetoencephalogram (MEG) data based on independent component analysis (ICA). The proposed features including kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used to identify the artifactual components such as cardiac, ocular, muscular, and sudden high-amplitude changes. For an ocular artifact, the frontal head region (FHR) thresholding was proposed. In this paper, ICA method was on the basis of FastICA algorithm to decompose the underlying sources in MEG data. Then, the corresponding ICs responsible for artifacts were identified by means of appropriate parameters. Comparison between MEG and artifactual components showed the statistical significant results at p < 0.001 for all features. The output artifact-free MEG waveforms showed the applicability of the proposed method in removing artifactual components.

本文言語English
ホスト出版物のタイトル2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
出版ステータスPublished - 2012
外部発表はい
イベント2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA, United States
継続期間: 2012 12月 32012 12月 6

出版物シリーズ

名前2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012

Other

Other2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
国/地域United States
CityHollywood, CA
Period12/12/312/12/6

ASJC Scopus subject areas

  • 情報システム

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