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

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

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Publication statusPublished - 2012 Dec 1
Event2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA, United States
Duration: 2012 Dec 32012 Dec 6

Publication series

Name2012 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
CountryUnited States
CityHollywood, CA
Period12/12/312/12/6

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ASJC Scopus subject areas

  • Information Systems

Cite this

Phothisonothai, M., Tsubomi, H., Kondo, A., Kikuchi, M., Yoshimura, Y., Minabe, Y., & Watanabe, K. (2012). Linear and nonlinear features for automatic artifacts removal from MEG data based on ICA. In 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 [6411790] (2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012).