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 MoreMetadata
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.
Published in: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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