A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging

Nunez, Paul L.; Silberstein, Richard B.; Cadusch, Peter J.; Wijesinghe, Ranjith S.; Westdorp, Andrew F.; Srinivasan, Ramesh

doi:10.1016/0013-4694(94)90112-0

Cited by 394 publications

(240 citation statements)

References 33 publications

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“…Surface Laplacians provide theoretically unique estimates of dura potential, subject to the usual practical limitations of spatial sampling and noise. Both simulations and application to experimental EEG data have shown that surface Laplacians are highly consistent with estimates of dura potential (dura imaging) obtained with 3 or 4-sphere volume conduction head models (Nunez et al, 1994;Nunez et al, 2001). The surface Laplacian enhances the sensitivity of EEG to sources that are "localized" in superficial cortex within a small distance (2-3 cm in all directions) of the electrode, and reduces the sensitivity of the EEG to deep sources and widespread synchronous sources.…”

Section: Surface Laplacian Ssvepsupporting

confidence: 52%

“…The details of the solution for dipole sources have been published previously (Srinivasan et al, 1998). This model has been used extensively in simulation studies of surface Laplacians (Nunez et al, 1994;Srinivasan et al, 1996), EEG coherence estimates (Nunez et al, 1997;Srinivasan et al, 1998), and forms the basis for a number of approaches to source localization (Lopes da Silva 2004). The thickness (or radii) and conductivity ratios between the spherical shells are the essential features of the model.…”

Section: Simulation Of Volume Conduction Effectsmentioning

confidence: 99%

“…The spatial resolution of EEG can be improved by estimating the surface Laplacian of scalp potentials (Perrin et al, 1987;Perrin et al, 1989;Law et al, 1993;Nunez et al, 1994;Srinivasan et al, 1996). Surface Laplacians provide theoretically unique estimates of dura potential, subject to the usual practical limitations of spatial sampling and noise.…”

Section: Surface Laplacian Ssvepmentioning

confidence: 99%

“…However, since both the surface Laplacian algorithm and Fourier analysis are linear transformations of the data, the surface Laplacian can be applied after application of the Fourier transform. In this study, we apply the New Orleans three-dimensional spline Laplacian algorithm (Law et al, 1993;Nunez et al, 1994;Srinivasan et al, 1996;Nunez and Srinivasan, 2005) to scalp potentials. At each temporal frequency, the real and imaginary parts of the Fourier coefficients at each electrode location were fit with a thirdorder thin-plate spline along the surface of the best-fit sphere to the electrode positions.…”

Section: Surface Laplacian Ssvepmentioning

confidence: 99%

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Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency

2006

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Steady-state visual evoked potentials (SSVEPs) are used in cognitive and clinical studies of brain function because of excellent signal-to-noise ratios and relative immunity to artifacts. SSVEPs also provide a means to characterize preferred frequencies of neocortical dynamic processes. In this study, SSVEPs were recorded with 110 electrodes while subjects viewed random dot patterns flickered between 3 and 30 Hz. Peaks in SSVEP power were observed at delta (3 Hz), lower alpha (7 and 8 Hz), and upper alpha band (12 and 13 Hz) frequencies; the spatial distribution of SSVEP power is also strongly dependent on the input frequency suggesting cortical resonances. We characterized the cortical sources that generate SSVEPs at different input frequencies by applying surface Laplacians and spatial spectral analysis. Laplacian SSVEPs recorded are sensitive to small changes (1-2 Hz) in the input frequency at occipital and parietal electrodes indicating distinct local sources. At 10 Hz, local source activity occurs in multiple cortical regions; Laplacian SSVEPs are also observed in lateral frontal electrodes. Laplacian SSVEPs are negligible at many frontal electrodes that elicit strong potential SSVEPs at delta, lower alpha, and upper alpha bands. One-dimensional (anteriorposterior) spatial spectra indicate that distinct large-scale source distributions contribute SSVEP power in these frequency bands. In the upper alpha band, spatial spectra indicate the presence of long-wavelength (> 15 cm) traveling waves propagating from occipital to prefrontal electrodes. In the delta and lower alpha band, spatial spectra indicate that long-wavelength source distributions over posterior and anterior regions form standing-wave patterns. These results suggest that the SSVEP is generated by both (relatively stationary) localized sources and distributed sources that exhibit characteristics of wave phenomena.

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Section: Surface Laplacian Ssvepsupporting

confidence: 52%

Section: Simulation Of Volume Conduction Effectsmentioning

confidence: 99%

Section: Surface Laplacian Ssvepmentioning

confidence: 99%

Section: Surface Laplacian Ssvepmentioning

confidence: 99%

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Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency

2006

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“…The essential feature of such models is low skull conductivity compared to brain, CSF, skull, or scalp tissue. This model has been used extensively in simulation studies of surface Laplacians (Nunez et al, 1994;Srinivasan et al, 1996), EEG coherence estimates (Nunez et al, 1997(Nunez et al, ,1999Srinivasan et al, 1998) and forms the basis for a number of approaches to source localization (Lopes da Silva 2004). The thickness and conductivity ratios of the spherical shells are the essential features of the model.…”

Section: Spatial Filtering Of Scalp Potentialsmentioning

confidence: 99%

Source analysis of EEG oscillations using high-resolution EEG and MEG

Srinivasan

Winter

Nunez

2006

Progress in Brain Research

112

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We investigated spatial properties of the source distributions that generate scalp electroencephalographic (EEG) oscillations. The inherent complexity of the spatiotemporal dynamics of EEG oscillations indicates that conceptual models that view source activity as consisting of only of a few "equivalent dipoles" are inadequate. We present an approach that uses volume conduction models to characterize the distinct spatial filtering of cortical source activity by averagereference EEG, high-resolution EEG, and magnetoencephalography (MEG). By comparing these three measures, we can make inferences about the sources of EEG oscillations without having to make prior assumptions about the sources. We apply this approach to spontaneous EEG oscillations observed with eyes closed at rest. Both EEG and MEG recordings show robust alpha rhythms over posterior regions of the cortex; however, the dominant frequency of these rhythms varies between EEG and MEG recordings. Frontal alpha and theta rhythms are generated almost exclusively by superficial radial dipole layers that generate robust EEG signals but very little MEG signals; these sources are presumably mainly in the gyral crowns of frontal cortex. MEG and high-resolution EEG estimates of alpha rhythms provide evidence of local tangential and radial sources in the posterior cortex, lying mainly on sulcal and gyral surfaces. Despite the detailed information about local radial and tangential sources potentially afforded by high-resolution EEG and MEG, it is also evident that the alpha and theta rhythms receive contributions from non-local source activity, for instance large dipole layers distributed over lobeal or (potentially) even larger spatial scales.

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Functional Brain Electrical Activity Mapping in Boys With Attention-Deficit/Hyperactivity Disorder

Silberstein

Farrow

Lévy

et al. 1998

Arch Gen Psychiatry

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A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging

Cited by 394 publications

References 33 publications

Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency

Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency

Source analysis of EEG oscillations using high-resolution EEG and MEG

Functional Brain Electrical Activity Mapping in Boys With Attention-Deficit/Hyperactivity Disorder

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