Boosting specificity of MEG artifact removal by weighted support vector machine

Fang Duan, Montri Phothisonothai, Mitsuru Kikuchi, Yuko Yoshimura, Yoshio Minabe, Kastumi Watanabe, Kazuyuki Aihara

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72%±0.67 of MEG ICs were preserved. The classification accuracy was 97.91%±1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41%±2.14.

本文言語English
ホスト出版物のタイトル2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
ページ6039-6042
ページ数4
DOI
出版ステータスPublished - 2013
外部発表はい
イベント2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
継続期間: 2013 7 32013 7 7

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period13/7/313/7/7

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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