Artifactual component classification from MEG data using support vector machine
- 1 December 2012
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
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
This publication has 15 references indexed in Scilit:
- The automatic detection of artifacts in Magnetoencephalographic signals based on fast ICA2011
- Automatic Removal of Eye-Movement and Blink Artifacts from EEG SignalsBrain Topography, 2009
- Dynamic effects and information quantifiers of statistical memory of MEG's signals at photosensitive epilepsyMathematical Biosciences and Engineering, 2009
- Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's DiseaseIEEE Transactions on Biomedical Engineering, 2008
- Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysisNeuroImage, 2008
- EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer InterfaceIEICE Transactions on Information and Systems, 2008
- Artifact Removal in Magnetoencephalogram Background Activity With Independent Component AnalysisIEEE Transactions on Biomedical Engineering, 2007
- Magnetoencephalographic artifact identification and automatic removal based on independent component analysis and categorization approachesJournal of Neuroscience Methods, 2006
- A Fast Fixed-Point Algorithm for Independent Component AnalysisNeural Computation, 1997
- On optimal and data-based histogramsBiometrika, 1979