Artifactual component classification from MEG data using support vector machine

Montri Phothisonothai; Fang Duan; Hiroyuki Tsubomi; Aki Kondo; Kazuyuki Aihara; Yuko Yoshimura; Mitsuru Kikuchi; Yoshio Minabe; Katsumi Watanabe

doi:10.1109/BMEiCon.2012.6465462

Artifactual component classification from MEG data using support vector machine

Montri Phothisonothai^*, Fang Duan, Hiroyuki Tsubomi, Aki Kondo, Kazuyuki Aihara, Yuko Yoshimura, Mitsuru Kikuchi, Yoshio Minabe, Katsumi Watanabe

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.

Original language	English
Title of host publication	5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
DOIs	https://doi.org/10.1109/BMEiCon.2012.6465462
Publication status	Published - 2012
Externally published	Yes
Event	5th 2012 Biomedical Engineering International Conference, BMEiCON 2012 - Muang, Ubon Ratchathani, Thailand Duration: 2012 Dec 5 → 2012 Dec 7

Publication series

Name	5th 2012 Biomedical Engineering International Conference, BMEiCON 2012

Other

Other	5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
Country/Territory	Thailand
City	Muang, Ubon Ratchathani
Period	12/12/5 → 12/12/7

Keywords

Artifacts
Independent component analysis
MEG
Magnetoencephalogram
Support vector machine

ASJC Scopus subject areas

Biomedical Engineering

Access to Document

10.1109/BMEiCon.2012.6465462

Cite this

Phothisonothai, M., Duan, F., Tsubomi, H., Kondo, A., Aihara, K., Yoshimura, Y., Kikuchi, M., Minabe, Y., & Watanabe, K. (2012). Artifactual component classification from MEG data using support vector machine. In 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012 Article 6465462 (5th 2012 Biomedical Engineering International Conference, BMEiCON 2012). https://doi.org/10.1109/BMEiCon.2012.6465462

Artifactual component classification from MEG data using support vector machine. / Phothisonothai, Montri; Duan, Fang; Tsubomi, Hiroyuki et al.
5th 2012 Biomedical Engineering International Conference, BMEiCON 2012. 2012. 6465462 (5th 2012 Biomedical Engineering International Conference, BMEiCON 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Phothisonothai, M, Duan, F, Tsubomi, H, Kondo, A, Aihara, K, Yoshimura, Y, Kikuchi, M, Minabe, Y & Watanabe, K 2012, Artifactual component classification from MEG data using support vector machine. in 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012., 6465462, 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012, 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012, Muang, Ubon Ratchathani, Thailand, 12/12/5. https://doi.org/10.1109/BMEiCon.2012.6465462

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title = "Artifactual component classification from MEG data using support vector machine",

abstract = "Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.",

keywords = "Artifacts, Independent component analysis, MEG, Magnetoencephalogram, Support vector machine",

author = "Montri Phothisonothai and Fang Duan and Hiroyuki Tsubomi and Aki Kondo and Kazuyuki Aihara and Yuko Yoshimura and Mitsuru Kikuchi and Yoshio Minabe and Katsumi Watanabe",

year = "2012",

doi = "10.1109/BMEiCon.2012.6465462",

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AU - Phothisonothai, Montri

AU - Duan, Fang

AU - Tsubomi, Hiroyuki

AU - Kondo, Aki

AU - Aihara, Kazuyuki

AU - Yoshimura, Yuko

AU - Kikuchi, Mitsuru

AU - Minabe, Yoshio

AU - Watanabe, Katsumi

PY - 2012

Y1 - 2012

N2 - Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.

AB - Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.

KW - Artifacts

KW - Independent component analysis

KW - MEG

KW - Magnetoencephalogram

KW - Support vector machine

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BT - 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012

T2 - 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012

Y2 - 5 December 2012 through 7 December 2012

ER -

Artifactual component classification from MEG data using support vector machine

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