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http://dx.doi.org/10.9718/JBER.2008.29.4.329

Comparison of ICA-based and MUSIC-based Approaches Used for the Extraction of Source Time Series and Causality Analysis  

Jung, Young-Jin (Department of Biomedical Engineering, Yonsei University)
Kim, Do-Won (Department of Biomedical Engineering, Yonsei University)
Lee, Jin-Young (Department of Biomedical Engineering, Yonsei University)
Im, Chang-Hwan (Department of Biomedical Engineering, Yonsei University)
Publication Information
Journal of Biomedical Engineering Research / v.29, no.4, 2008 , pp. 329-336 More about this Journal
Abstract
Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.
Keywords
Directed transfer function (DTF); Electroencephalogram (EEG); Forward/Inverse problem; Granger causality (GC); Independent component analysis (ICA); Multiple signal classification (MUSIC); Nonlinear analysis;
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