Sensitivity distributions of EEG and MEG measurements

Malmivuo, Jaakko; Suihko, V.; Eskola, Hannu

doi:10.1109/10.554766

Cited by 125 publications

(76 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The temporal resolution of EEG is identical to that of MEG, and both are superior to hemodynamic measures by two orders of magnitude (milliseconds vs. seconds). Whether MEG confers advantages in spatial resolution over the EEG is now a matter ofdebate (Cohen et al, 1990;Malmivuo, Suihko, & Eskola, 1997;Wikswo et al, 1993). Coupled with advances in electrical source localization, dense sensor array EEG may be the most cost-effective approach to neuroimaging now available, adding critical anatomical context to the rich temporal information of scalp electrical recordings.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Estimating the spatial Nyquist of the human EEG

Srinivasan

Tucker

Murias

1998

Behavior Research Methods, Instruments, & Computers

227

149

View full text Add to dashboard Cite

The discrete sampling of the brain's electrical field at the scalp surface with individual recording sensors is subject to the same sampling error as the discrete sampling of the time series at anyone sensor with analog-to-digital conversion. Unlike temporal sampling, spatial sampling is intrinsically discrete, so that the post hoc application of analog anti-aliasing filters is not possible. However, the skull acts as a low-pass spatial filter ofthe brain's electrical field, attenuating the high spatial frequency information. Because of the skull's spatial filtering, a discrete sampling of the spatial field with a reasonable number of scalp electrodes is possible. In this paper, we provide theoretical and experimental evidence that adequately sampling the human electroencephalograph (EEG) across the full surface of the head requires a minimum of 128 sensors. Further studies with each of the major EEG and event-related potential phenomena are required in order to determine the spatial frequency of these phenomena and in order to determine whether additional increases in sensor density beyond 128 channels will improve the spatial resolution of the scalp EEG.When the time series ofan electroencephalogram (EEG) channel is sampled discretely, the Nyquist theorem specifies that the highest measurable frequency is half the sampling rate. For example, with a 250 sample/sec analog-to-digital conversion rate, the highest frequency that can be resolved is 125 Hz. In actuality, because of phase alignment, it is necessary to discretely sample (digitize) the signal at a rate at least 2.5 times the highest frequency component ofthe signal (Bendat & Piersol, 1986). Signal frequencies higher than the Nyquist frequency are not only poorly characterized; they alias or appear misleadingly as increased energy at lower frequencies. To avoid aliasing, it is necessary to eliminate the frequency components of the signal that are higher than the Nyquist frequency through analog filtering.The electrical field of the brain generates a potential distribution that is continuous over the surface of the head. The discretization ofthis spatial EEG or averaged eventrelated potential (ERP) signal with scalp electrodes is also subject to the Nyquist theorem. Ifthe spatial sampling is Correspondence concerning this article should be addressed to D. M.Tucker, Electrical Geodesics, Inc., Riverfront Research Park, 1811 Garden Avenue, Suite 104, Eugene, OR 97403 (e-mail: dtucker@egi.com).Note: ElectricalGeodesics, Inc. sells 64-, 128-,and 256-channelEEG systems and thus has a commercial interest in promoting dense sensor array technology.-Editor

show abstract

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Estimating the spatial Nyquist of the human EEG

Srinivasan

Tucker

Murias

1998

Behavior Research Methods, Instruments, & Computers

227

149

View full text Add to dashboard Cite

show abstract

“…Even the modeling studies, however, have been equivocal. Some modeling work found MEG to be more accurate than EEG [Murro et al, 1995;Stok, 1987], whereas others found EEG and MEG accuracy to be comparable [Malmivuo et al, 1997], or EEG accuracy better than MEG [Mosher et al, 1993;Pascual-Marqui and Biscay-Lirio, 1993].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo simulation studies of EEG and MEG localization accuracy

Liu¹,

Dale²,

Belliveau³

2002

Human Brain Mapping

330

256

View full text Add to dashboard Cite

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.

show abstract

“…The earlier results with relative skull resistivities of 1/80/1 and 1/100/1 are also given. The results are thus comparable with those of our earlier paper [3]. …”

supporting

confidence: 82%

Effect of skull resistivity on the relative sensitivity distributions of EEG and MEG measurements

Malmivuo

Suihko²

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

Abstract-We have previously published calculations which show that, contrary to what has been believed, despite the high resistivity of the skull the spatial sensitivity of magnetoencephalography, MEG, is no better than that of electroencephalography, EEG. The results were based on the widely used Rush-Driscoll head model, where skull resistivity is considered to be 80 times that of the brain and the scalp.Recent research indicated that the skull resistivity is only about 15 times that of the brain and scalp. Calculations of EEG sensitivity distributions with this value show that EEG has considerably better spatial resolution than MEG. Since clinical recordings are not in conflict with such a result, the conclusion can be considered reliable. The finding supports use of highresolution EEG as research and clinical tool in recording the electric activity of the brain. KeywordsBioelectromagnetism, electroencephalography, magnetoencephalographyThe electric activity of the brain generates both electric and magnetic fields, detected as electroencephalography, EEG, and magnetoencephalography, MEG. Both of these techniques are nowadays used as research and clinical tools. For the benefit of the brain research it is important to discuss about the relative merits of these techniques. In this discussion there exist several issues, theoretical and technical. One of these issues is the spatial resolution.We have previously demonstrated with calculations with the Rush-Driscoll model that the spatial resolution of the axial gradiometer is an order of magnitude poorer than that of a bipolar EEG measurement. The spatial resolution of a planar gradiometer in the Rush-Driscoll model is about the same order as that of the bipolar EEG. [1 -4] It has recently been demonstrated with several different approaches that the earlier conception of the high resistivity of the skull is overestimated and that the correct ratio between the resistivity of the skull and that of the brain and scalp tissues is 15/1 [5]. We recalculated the most central results of our previous study with this resistivity ratio to obtain more reliable information on the relative spatial resolutions of the EEG and MEG. II. METHODSTo investigate the EEG and MEG detectors' ability to concentrate their measurement sensitivity we use the concept half-sensitivity volume (HSV). The HSV is the volume of the source region in which the magnitude of the detector's sensitivity is more than one half of its maximum value in the source region [1]. If a source is homogeneously distributed, the smaller the HSV is, the smaller is the region from which the detector's signal originates.In comparing the EEG and MEG detectors' merits the criterion has usually been either their accuracy in localizing a source dipole or in differentiating between two nearby dipoles. In a clinical measurement, however, a neurologist is interested in measuring the electric activity of brain tissue from a limited region. That is a volume source, not a discrete dipole. These are, of course, mathematically rel...

show abstract

Sensitivity distributions of EEG and MEG measurements

Cited by 125 publications

References 13 publications

Estimating the spatial Nyquist of the human EEG

Estimating the spatial Nyquist of the human EEG

Monte Carlo simulation studies of EEG and MEG localization accuracy

Effect of skull resistivity on the relative sensitivity distributions of EEG and MEG measurements

Contact Info

Product

Resources

About