Monte Carlo simulation studies of EEG and MEG localization accuracy

Abstract

Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity. The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (i.e., the forward model). Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization. Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model. We evaluated its accuracy using the crosstalk and point spread metrics. The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations. The point spread metric provides the complementary measure: for that same location, the point spread describes the mis-localization of activity from that specified location to other locations in the brain. We also propose and examine the utility of a "noise sensitivity normalized" inverse operator. Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread. Hum. Brain Mapping 16:47-62, 2002.

Dealing with mismatched fMRI activations in fMRI constrained EEG cortical source imaging: a simulation study assuming various mismatch types

“…The source distribution can be estimated by multiplying the measured signal at a specific instant x by W. If we assume that both R and C are scalar multiples of an identity matrix, this approach becomes identical to minimum norm estimation [28]. In this study, the source covariance matrix R was assumed to be a diagonal matrix, which means that we ignored the relationships between neighboring sources.…”

“…Moreover, the use of the fMRI prior information can reduce spurious or phantom sources generated due to the illposedness of EEG/MEG inverse problems. On the other hand, the EEG or MEG can provide temporal information for the static fMRI results [5,8,[26][27][28].…”

Evaluation of Algorithms for Intracranial EEG (iEEG) Source Imaging of Extended Sources: Feasibility of Using iEEG Source Imaging for Localizing Epileptogenic Zones in Secondary Generalized Epilepsy

“…The pre-calculated leadfield matrix was used to generate artificial iEEG datasets as well as to reconstruct cortical source distributions. Four different cortical sourceimaging algorithms were evaluated using the simulated iEEG datasets any iterative processes (please refer to Liu et al 2002 for various forms of the linear inverse operators). The L p -norm estimation can be regarded as a general form of MNE that uses L p -norm (1 B p B 2) instead of a Euclidean norm (p = 2) in its formulations.…”

An EEG-based real-time cortical functional connectivity imaging system

“…If we assume that both R and C are scalar multiples of identity matrix, this approach becomes identical to minimum norm estimation [34]. In this study, the source covariance matrix R was assumed to be an identity matrix, which means that we ignored relationships between neighboring sources.…”