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- W2005774503 abstract "One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, “extra-dipole,” that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness. • We evaluated our proposed method by estimating cortical and extra-brain currents. • The cortical and extra-brain currents were accurately estimated with a simulation. • The performance of this method was better than conventional approaches. • We applied our proposed method to measured MEG data and confirmed its effectiveness." @default.
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- W2005774503 date "2014-11-01" @default.
- W2005774503 modified "2023-09-24" @default.
- W2005774503 title "Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem" @default.
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- W2005774503 doi "https://doi.org/10.1016/j.neuroimage.2014.07.010" @default.
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