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.

Human posterior auditory cortex gates novel sounds to consciousness

“…4 shows estimated neural activity underlying anterior and posterior N1 responses within the left auditory cortex. A combination of fMRI (experiment 3) and MEG (experiment 2) data were used to calculate these estimates (24)(25)(26). Neural activity at the latency of the posterior N1 response was estimated to encompass Heschl's gyrus (HG), planum temporale (PT), superior temporal sulcus (STS), middle temporal gyrus (MTG), and the posterior aspects of superior temporal gyrus (STG).…”

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

“…Similar to procedures used in MEG/EEG source analysis (Dale et al, 2000;Liu et al, 1998;Liu et al, 2002), we estimated the average point spread function (aPSF) at each location to quantify the spatial spread of the reconstruction: [Eq. 13] where |d i (ρ ⃑ )| indicates the distance between source location i and source location ρ ⃑ .…”

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

“…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 identity matrix, this approach becomes identical to minimum norm estimation (Liu et al, 2002). In the present study, the source covariance matrix R was assumed to be a diagonal matrix, which means that we ignored relationships between neighboring sources.…”