Image Processing Reference
In-Depth Information
The similar nature of the EEG and MEG signals means that many methods of
data analysis are applicable to both E/MEG modalities. Although the SNR of E/MEG
signals have improved with technological advances, and some basic analysis has
been performed by experts on raw E/MEG data via visual inspection of spatial signal
patterns outside of the brain, more advanced methods are required to use data
efficiently. During the last two decades, many E/MEG signal analysis techniques
[10] have been developed to provide insights on different levels of perceptual and
cognitive processing of the human brain: event-related potential (ERP) in EEG and
event-related field (ERF) in MEG, components analysis (PCA, ICA, etc.), frequency
domain analysis, pattern analysis, single-trial analysis [11-13], etc. Source localiza-
tion techniques were first developed for MEG because the head model required for
forward modeling of the magnetic field is relatively simple. Source localization using
an EEG signal has been difficult to perform because the forward propagation of the
electric potentials is more complicated. However, recent advances in automatic MRI
segmentation methods, together with advances in forward and inverse EEG model-
ing, have made EEG source localization plausible.
The theory of electromagnetism also explains why EEG and MEG signals can
be considered complementary, in that they provide different views of the same
physiological phenomenon [6,14,15]. On the one hand, an often-accented difference
is that MEG is not capable of registering the magnetic field generated by the sources
that are oriented radially to the skull surface in the case of spherical conductor
geometry. On the other hand, MEG has the advantage over EEG in that the local
variations in conductivity of different brain matter (e.g., white matter, gray matter)
do not attenuate the MEG signal much, whereas the EEG signal is strongly influ-
enced by the skull and different types of brain matter [8]. The orientation selectivity,
combined with the higher depth precision due to homogeneity, makes MEG optimal
for detecting activity in sulci (brain fissures) rather than in gyri (brain ridges). In
contrast, a registered EEG signal is dominated by the gyral sources close to the
skull and therefore is more radial to its surface. Yet another crucial difference is
dictated by basic physics. The orthogonality of magnetic and electrical fields leads
to orthogonal maps of the magnetic field and electrical potential on the scalp surface.
This orthogonality means that an orthogonal localization direction is the best
localization direction for both modalities [15,16]. These complementary features
of the EEG and MEG signals are what make them good candidates for integration
[17,18]. The conjoint E/MEG analysis has improved the fidelity of EMSI localiza-
tion, but has not entirely solved the problem of source localization ambiguity. It is
in the reduction of this remaining ambiguity that information from other brain
imaging modalities may play a valuable role.
It is worth noting another purely technical advantage of MEG over EEG: MEG
provides a reference-free recording of the actual magnetic field. Whenever EEG
sensors capture scalp potentials, a reference electrode must be used as a ground to
derive the signal of interest. A reference signal chosen in such a way can be arbitrarily
biased relative to the EEG signal observed, even when no neuronal sources are active.
The unknown in an MEG signal obtained using SQUID sensors is just a constant
in time offset—the DC baseline. This baseline depends on the nearest flux quantum
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