TY - GEN
T1 - Artifactual component classification from MEG data using support vector machine
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
UR - http://www.scopus.com/inward/record.url?scp=84875092421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875092421&partnerID=8YFLogxK
U2 - 10.1109/BMEiCon.2012.6465462
DO - 10.1109/BMEiCon.2012.6465462
M3 - Conference contribution
AN - SCOPUS:84875092421
SN - 9781467348928
T3 - 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
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 -