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Boosting specificity of MEG artifact removal by weighted support vector machine | IEEE Conference Publication | IEEE Xplore

Boosting specificity of MEG artifact removal by weighted support vector machine


Abstract:

An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and ...Show More

Abstract:

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.
Date of Conference: 03-07 July 2013
Date Added to IEEE Xplore: 26 September 2013
Electronic ISBN:978-1-4577-0216-7

ISSN Information:

PubMed ID: 24111116
Conference Location: Osaka, Japan

References

References is not available for this document.