TY - GEN
T1 - Linear and nonlinear features for automatic artifacts removal from MEG data based on ICA
AU - Phothisonothai, Montri
AU - Tsubomi, Hiroyuki
AU - Kondo, Aki
AU - Kikuchi, Mitsuru
AU - Yoshimura, Yuko
AU - Minabe, Yoshio
AU - Watanabe, Kastumi
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84874443207
SN - 9780615700502
T3 - 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
BT - 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
T2 - 2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Y2 - 3 December 2012 through 6 December 2012
ER -