Mapping the Signal-To-Noise-Ratios of Cortical
Sources in Magnetoencephalography
and Electroencephalography
Daniel M. Goldenholz,1,2* Seppo P. Ahlfors,1,3 Matti S. Ha¨ma¨la¨inen,1,3
Dahlia Sharon,1 Mamiko Ishitobi,1 Lucia M. Vaina,1,2,4 and Steven M. Stufflebeam1,3
1Athinoula A. Martinos Center For Biomedical Imaging, Massachusetts General Hospital,
Charlestown, Massachusetts
2Biomedical Engineering Department, Boston University, Boston, Massachusetts
3Harvard-MIT Division of Health Science and Technology, Massachusetts Institute of Technology,
Cambridge, Massachusetts
4Department of Neurology, Harvard Medical School, Boston, Massachusetts
Abstract: Although magnetoencephalography (MEG) and electroencephalography (EEG) have been
available for decades, their relative merits are still debated. We examined regional differences in
signal-to-noise-ratios (SNRs) of cortical sources in MEG and EEG. Data from four subjects were used to
simulate focal and extended sources located on the cortical surface reconstructed from high-resolution
magnetic resonance images. The SNR maps for MEG and EEG were found to be complementary. The
SNR of deep sources was larger in EEG than in MEG, whereas the opposite was typically the case for
superficial sources. Overall, the SNR maps were more uniform for EEG than for MEG. When using a
noise model based on uniformly distributed random sources on the cortex, the SNR in MEG was found
to be underestimated, compared with the maps obtained with noise estimated from actual recorded
MEG and EEG data. With extended sources, the total area of cortex in which the SNR was higher in
EEG than in MEG was larger than with focal sources. Clinically, SNR maps in a patient explained dif-
ferential sensitivity of MEG and EEG in detecting epileptic activity. Our results emphasize the benefits
of recording MEG and EEG simultaneously. Hum Brain Mapp 30:1077–1086, 2009. VC 2008 Wiley-Liss, Inc.
Key words: MEG; EEG; epilepsy; forward model; SNR; simulation; dipole; cortex; brain
INTRODUCTION
Although a growing number of clinical methods are
available for observing brain activity noninvasively, only
two provide millisecond scale temporal resolution with
centimeter or better spatial resolution: electroencephalogra-
phy (EEG) and magnetoencephalography (MEG). Despite
being generated by similar neural sources, MEG and EEG
signals differ, suggesting that recording both simultane-
ously is beneficial [Babiloni et al., 2004; Barkley and Baum-
gartner, 2003; Iwasaki et al., 2005; Knake et al., 2006; Leijten
et al., 2003; Lesser, 2004; Lin et al., 2003; Lopes da Silva
et al., 1991; Pataraia et al., 2005; Wood et al., 1985; Yoshi-
naga et al., 2002; Zijlmans et al., 2002]. However, partly
Contract grant sponsor: National Center for Research Resources;
Contract grant number: P41RR14074; Contract grant sponsor: NIH;
Contract grant numbers: NS37462 and NS44623; Contract grant
sponsor: Mental Illness and Neuroscience Discovery (MIND)
Institute.
*Correspondence to: Daniel Goldenholz, MGH-Martinos Center,
Building 149, 13th St., Charlestown, MA 02129.
E-mail: daniel@nmr.mgh.harvard.edu
Received for publication 13 February 2007; Revised 17 September
2007; Accepted 22 February 2008
DOI: 10.1002/hbm.20571
Published online 8 May 2008 in Wiley InterScience (www.
interscience.wiley.com).
V
C
2008 Wiley-Liss, Inc.
r Human Brain Mapping 30:1077–1086 (2009) r

due to the higher cost of an MEG system, neurophysiolo-
gists and clinicians are hesitant to adopt the technology,
citing studies that conclude the differences in the source
localization accuracy between MEG and EEG are minor
[Baumgartner 2004; Cohen et al., 1990; Liu et al., 2002].
Conversely, those with experience in MEG often do not re-
cord EEG simultaneously, relying on evidence of MEG’s
more precise localization capabilities [Ha¨ma¨la¨inen et al.,
1993; Leahy et al., 1998], thereby reducing substantially the
pre-recording preparation time required for each subject.
An important potential application of MEG and EEG
source localization is the presurgical evaluation of epileptic
patients. Epilepsy affects up to 1% of the population in
North America [Wiebe et al., 2001]. Approximately 20% of
patients do not achieve adequate control of seizures with
medication; many of them are candidates for surgery that
can reduce or eliminate seizures [Pataraia et al., 2002;
Wiebe et al., 2001]. Surgical outcomes are improved by
identifying and locating epileptic spikes in MEG and/or in
EEG [Ebersole and Pedley, 2002; Stefan et al., 2003]. An ep-
ileptic spike must exhibit high enough signal-to-noise-ratio
(SNR) to be distinguished from background noise [Cobb,
1983; Iwasaki et al., 2005], and to be localized accurately
[Fuchs et al., 1998; Tarkiainen et al., 2003]. The SNR of
observed brain activation in EEG and MEG depends not
only on the sensor characteristics (type, placement, noise),
but also on the location and orientation of the source.
A number of studies have examined sensitivities and
expected SNR values of MEG and/or EEG [de Jong et al.,
2005; Fuchs et al., 1998; Hillebrand and Barnes, 2002; Tar-
kiainen et al., 2003]. The present study extends and
expands on previous work by incorporating the realistic
shape of the cortical surface to impose an anatomical con-
straint on the locations and orientations of the sources. We
examined the difference in SNR between MEG and EEG
for each cortical location using both actual noise record-
ings and a simple noise model to better understand the
generalizability of the results. We also compared the SNR
of individual dipole sources to that of extended cortical
patch sources, and related our findings to the differential
detection of epileptic spikes in MEG and EEG.
METHODS
Data Acquisition
Four right-handed subjects (two females), aged 23–42,
were included in this study. Each signed a consent form
and a privacy statement in accordance with our Institu-
tional Human Subject Research Board and HIPAA stand-
ards. High-resolution anatomical magnetic resonance
images (MRI) were obtained with a Trio 3T scanner [Sie-
mens Medical Systems, Erlangen, Germany] using
MPRAGE pulse sequences (voxel size 1.3 3 1.0 3 1.3
mm3; slice thickness 1.3 mm; TE 3.31 ms; TR 2530 ms;
gap 50%; FOV 10 cm 3 10 cm) and FLASH images (flip
angle 58; slice thickness 1.3 mm; FOV 10 cm 3 10 cm).
MEG and EEG data were acquired simultaneously with a
VectorviewTM system [Elekta-Neuromag, Helsinki, Fin-
land] from 204 planar gradiometers, 102 magnetometers,
and 70 EEG electrodes (see Fig. 1). Two minutes of spon-
taneous activity was recorded, sampled at 600 Hz with
hardware filters set at 0.1–200 Hz (Subjects 1 and 2) or
0.03–200 Hz (Subjects 3 and 4). The EEG data was trans-
formed to the average electrode reference. Signal-space
projection (SSP) was applied to magnetometer data
[Tesche et al., 1995] to reduce background environmental
noise. The noise subspace for SSP was selected as the
three-dimensional space spanned by the eigenvectors cor-
responding to the three largest eigenvalues of the correla-
tion matrix of a 5-min recording of data collected without
a subject present; these correspond approximately to the
homogeneous field components, typically originating
from environmental (e.g., moving vehicles) sources far
from the sensors. The locations of fiducial head points,
EEG electrodes, and head-position indicator (HPI) coils
were digitized using a FastTrack 3D digitizer [Polhemus,
USA]. The location of the MEG sensor array with respect
to the head was determined at the beginning of each
measurement from the magnetic fields generated by the
HPI coils.
The Forward Model
For forward modeling, we used a linear collocation
three-layer boundary-element method with conductivity
values: 0.3, 0.06, and 0.3 S/m for the brain, skull and scalp,
respectively [Ha¨ma¨la¨inen and Sarvas, 1989; Ha¨ma¨la¨inen
et al., 1993]. The surface of the skin, as well as the inner
and outer surfaces of the skull were determined from the
MRIs (see Fig. 1). Each surface was tessellated with 5120
triangles, providing adequate numerical accuracy [Crou-
zeix et al., 1999; de Jongh, 2005; Fuchs et al., 2001; Tarkiai-
nen, 2003].
The cortical surface was reconstructed from the MRI
data with about 130,000 vertices per hemisphere and an
approximate vertex-to-vertex spacing of 1 mm using the
Freesurfer software [Dale et al., 1999; Fischl et al., 1999a;
Fischl et al., 2001; Segonne et al., 2004]. We computed the
MEG and EEG signals predicted by the forward model for
dipole sources oriented perpendicular to the gray-white
matter boundary at each of the surface vertices.
Neural sources were assumed to be either point sources
(current dipoles) or synchronously active patches. For
dipolar sources, the source amplitude was 10 nAm; for
patches, source strength was chosen to correspond to a
uniform surface source density of 50 pAm/mm2 [Hille-
brand and Barnes, 2002; Lu and Williamson, 1991]. To cre-
ate extended sources, the cortical surface was divided into
two complete sets of centroids using the geodesic distance-
weighted Dijkstra algorithm [Dijkstra, 1959]. The source
patches had geodesic radii of 10 mm or 16 mm, corre-
sponding to areas of 3 cm2 or 8 cm2, respectively.
r Goldenholz et al. r
r
1078
r