Monte Carlo Simulation Studies of EEG and MEG Localization Accuracy

Massachusetts General Hospital, NMR Center, Building 149, 13th Street, Charlestown, MA 02129, USA.
Human Brain Mapping (Impact Factor: 5.97). 05/2002; 16(1):47-62. DOI: 10.1002/hbm.10024
Source: PubMed


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.

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    • "In this approach, the MEG and EEG source locations are restricted to the cortical mantle derived from anatomical MRI to reduce the potential solution space (Dale and Sereno, 1993). Additional improvements are achieved by combining the complementary information provided by simultaneously measured MEG and EEG, which helps provide better accuracy and smaller point spread of the source estimates than either modality alone (Ding and Yuan, 2013; Henson et al., 2009; Liu et al., 2002; Sharon et al., 2007). "
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    NeuroImage 09/2015; 124(Pt A). DOI:10.1016/j.neuroimage.2015.09.044 · 6.36 Impact Factor
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    • "A recent successful approach to the problem of noise reduction of EEG signals is independent component analysis (ICA), which decomposes a multi-channel signal in a set of sources with maximally independent components (ICs). However, it has limitation on the number of separable ICs, N ICs from N electrodes, which makes the decomposition imperfect as the number of EEG sources is much higher than the number of ICs [3], [4], [5]. In addition, ICA-based methods require subjective decision making [6] or arbitrary tuning of the thresholds [7] to distinguish artifacted ICs from non-artifacted ICs, which is known as the permutation problem. "
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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy; 08/2015
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    • "Due to the spatial resolution of $1 cm, the MNE for a focal source can extend across sulcal walls separated by only a few millimetres (Liu et al., 2002; Lin et al., 2006; Hauk et al., 2011). In our case, the problem is somewhat worsened by the intersubject variability in S1 localization on the central sulcus, as illustrated in Supplementary Fig. 1. "
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