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发表刊物：The 2012 Biomedical Engineering International Conference (BMEiCON-2012)

关键字：Magnetoencephalogram, MEG, support vector machine, independent component analysis, artifacts

摘要：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.

论文类型：会议论文

是否译文：否

发表时间：2012-10-01

收录刊物：EI

第一作者：Montri Phothisonothai

合写作者：段放,Hiroyuki Tsubomi,Aki Kondo,Kazuyuki Aihara,Yuko Yoshimura,Mitsuru Kikuchi,Yoshio Minabe,Katsumi Watanabe