Comparison of the magnetoencephalogram and electroencephalogram

Cuffin, B. Neil; Cohen, David B.

doi:10.1016/0013-4694(79)90215-3

Cited by 236 publications

(126 citation statements)

References 19 publications

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“…There are several similarities and differences between EEG and MEG that have been discussed thoroughly in prior work [4][5][6][7][8]. The differences are briefly highlighted here.…”

Section: Introductionmentioning

confidence: 88%

Sensitivity of EEG and MEG to the N1 and P2 Auditory Evoked Responses Modulated by Spectral Complexity of Sounds

et al. 2007

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Acoustic complexity of a stimulus has been shown to modulate the electromagnetic N1 (latency ~110 ms) and P2 (latency ~190 ms) auditory evoked responses. We compared the relative sensitivity of electroencephalography (EEG) and magnetoencephalography (MEG) to these neural correlates of sensation. Simultaneous EEG and MEG were recorded while participants listened to three variants of a piano tone. The piano stimuli differed in their number of harmonics: the fundamental frequency (f 0 ), only,or f 0 and the first two or eight harmonics. The root mean square (RMS) of the amplitude of P2 but not N1 increased with spectral complexity of the piano tones in EEG and MEG. The RMS increase for P2 was more prominent in EEG than MEG, suggesting important radial sources contributing to the P2 only in EEG. Source analysis revealing contributions from radial and tangential sources was conducted to test this hypothesis. Source waveforms revealed a significant increase in the P2 radial source amplitude in EEG with increased spectral complexity of piano tones. The P2 of the tangential source waveforms also increased in amplitude with increased spectral complexity in EEG and MEG. The P2 auditory evoked response is thus represented by both tangential (gyri) and radial (sulci) activities. The radial contribution is expressed preferentially in EEG, highlighting the importance of combining EEG with MEG where complex source configurations are suspected.

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“…There are several similarities and differences between EEG and MEG that have been discussed thoroughly in prior work [4][5][6][7][8]. The differences are briefly highlighted here.…”

Section: Introductionmentioning

confidence: 88%

Sensitivity of EEG and MEG to the N1 and P2 Auditory Evoked Responses Modulated by Spectral Complexity of Sounds

et al. 2007

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“…However, differences in the physical properties of the electric and magnetic fields arising from the same current source may help improve the source localization using both measurements, relative to using either of them alone (Cohen and Cuffin, 1983;Cuffin and Cohen, 1979). The spatial pattern of the electric potential and that of the normal component of the magnetic field produced by a focal tangential current source are rotated by 90 degrees relative to each other, so that the axes that give the best localization accuracy are also orthogonal (Cohen and Cuffin, 1983).…”

Section: Introductionmentioning

confidence: 99%

“…These complementary properties of EEG and MEG allowed the disambiguation of the early evoked potential in response to median nerve stimulation, and its identification as a single tangential source in somatosensory cortex, rather than a pair of radial sources in motor and somatosensory cortices (Wood et al, 1985). In addition, the sensitivity pattern of MEG decreases more rapidly with source depth than that of EEG (Cuffin and Cohen, 1979). Finally, the high resistivity of the skull in combination with the more conductive scalp smears and attenuates the electrical potential distribution (Cooper et al, 1965;Delucchi et al, 1962;Geisler and Gerstein, 1961), whereas it does not affect the magnetic fields (Grynszpan and Geselowitz, 1973); this confers a localization advantage for MEG, for those sources that it is able to detect (Cuffin and Cohen, 1979).…”

Section: Introductionmentioning

confidence: 99%

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The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

et al. 2007

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To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously, and compared with the functional MRI (fMRI) localization produced by identical stimuli, in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities, rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for highresolution spatiotemporal studies of the human brain.

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“…Our model here assumes an isotropic volume conductor of spherical geometry composed of four homogeneous layers, each with a different conductivity (Cuffin and Cohen 1979). The layers model scalp, skull, cerebro-spinal fluid (CSF), and brain with…”

Section: Modelling the Volume Conductormentioning

confidence: 99%

Mapping EEG-potentials on the surface of the brain: A strategy for uncovering cortical sources

et al. 1997

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Summary: f i s paper describes a u d o r m method for calculating the interpolation of scalp EEG potential distibution, the current soum density (CSD), the cortical potentml distribution (coacal mapping) and the CSD ot the cortical potential distribution. It will be shown that interpolation and deblumng methods such as CSD or cortical mappmg are not independent of the inverse problem in potenlid theory. Not only the resolution but also the accuracy oft hese tec.hniques, especially those of deblurring, depend greatly on the spatial sampling rate (i.e., the number of elechades). Us~ng examples from slrnulated and real (64 channels) data it can be shown h a t the application of mom than 1GU EEC channels is not only favourable but necess171-y to guarantee a reasonable accuracy in the calculations of CSD or cortical mapping. Likewise, it can be shown tkat using nmre than 250 electrodes does not improve the resolution.Key words: High spatial sampling EEG; Sparial det?lumg; Current source density; Corncal Mapping: Spherical spline i n k r p~l a t i o~ lnversepmblem.

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Comparison of the magnetoencephalogram and electroencephalogram

Cited by 236 publications

References 19 publications

Sensitivity of EEG and MEG to the N1 and P2 Auditory Evoked Responses Modulated by Spectral Complexity of Sounds

Sensitivity of EEG and MEG to the N1 and P2 Auditory Evoked Responses Modulated by Spectral Complexity of Sounds

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

Mapping EEG-potentials on the surface of the brain: A strategy for uncovering cortical sources

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