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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Morishige K,Yoshioka T,Kawawaki D,Hiroe N,Sato MA,Kawato Mdoi
10.1016/j.neuroimage.2014.07.010subject
Has Abstractpub_date
2014-11-01 00:00:00pages
320-36eissn
1053-8119issn
1095-9572pii
S1053-8119(14)00582-5journal_volume
101pub_type
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