• Computer Science
  • Published in
    Proceedings of The Asia…
    2012

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

@article{Phothisonothai2012LinearAN,
  title={Linear and nonlinear features for automatic artifacts removal from MEG data based on ICA},
  author={Montri Phothisonothai and Hiroyuki Tsubomi and Aki Kondo and Mitsuru Kikuchi and Yuko Yoshimura and Yoshio Minabe and Katsumi Watanabe},
  journal={Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference},
  year={2012},
  pages={1-9}
}
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… CONTINUE READING

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Boosting specificity of MEG artifact removal by weighted support vector machine

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