High resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods

Gevins, Alan; Jiang, Le; Martin, Nancy K.; Brickett, P.; Desmond, John E.; Reutter, B.W.

doi:10.1016/0013-4694(94)90050-7

Cited by 237 publications

(113 citation statements)

References 21 publications

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“…To perform the analysis of sources of various kinds of cortical phenomena, including epileptiform spikes, using high-resolution EEGs Gevins and collaborators [29] introduced a deblurring operation, i.e., a method to minimize the blur distortion that takes place in the transfer from the cortical surface to the scalp, using a realistic finite-element model of the MRI of the subject's head. According to this approach, the scalp and skull are approximated by tetrahedral finite elements having the physical properties characteristic of the different layers of the head, and the algorithm searches for the optimal potential distribution at the cortical surface that provides the best-fit forward solution to the measured scalp distribution, using an appropriate volume conductor model (see below).…”

Section: B Requirements For Time and Spatial Samplingmentioning

confidence: 99%

“…Therefore, there is a need to know the head and brain shapes, thickness of the skull and other tissue layers to improve the quality and accuracy of the estimates of inverse solutions. Accordingly, Nunez et al [86] discussed the intricacies of mathematical models of the skull and brain volumes, and Gevins et al [29] used corrected and aligned MRI images to recreate a realistic head model. The possibility of using detailed MRI images to model the gyral pattern amenable to mathematical solutions has been explored [2].…”

Section: F Volume Conductor Modelsmentioning

confidence: 99%

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The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Silva

2008

IEEE Rev. Biomed. Eng.

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Section: B Requirements For Time and Spatial Samplingmentioning

confidence: 99%

Section: F Volume Conductor Modelsmentioning

confidence: 99%

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Silva

2008

IEEE Rev. Biomed. Eng.

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“…On the other hand, if the number of ECDs used in the dipole source localization is not correct, then misleading results maybe obtained. For this reason, distributed source imaging approaches that do not require a priori knowledge on the number of source dipoles have been investigated (Hamalainen & Ilmoniemi, 1984;Dale & Sereno, 1993;Gevins et al, 1994;Pascual-Marqui et al, 1994;Babiloni et al, 1997Babiloni et al, ,2001He et al, 1999He et al, ,2001He et al, ,2002aHe et al, , 2002bZhang et al, 2003). Therefore, development of methods being able to estimate reliably the number of source dipoles is of importance for further enhancing dipole source localization and ultimate improving our ability to image and localize brain electrical sources from noninvasive electromagnetic measurements.…”

Section: Discussionmentioning

confidence: 99%

On the estimation of the number of dipole sources in EEG source localization

Xiang¹,

He²

2005

Clinical Neurophysiology

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Background-The purpose of the present study was to determine the number of the equivalent dipole sources corresponding to the scalp EEG using the information criterion method based on the instantaneous-state modeling.Methods-A three-concentric-spheres head model was used to represent the head volume conductor. The Powell algorithm was used to solve the inverse problem of estimating the equivalent dipoles from the scalp EEG. The information criterion with different penalty functions was used to determine the dipole number. Computer simulations were conducted to evaluate effects of various parameters on the estimation of dipole number.Results-The present results suggest that the present method is able to estimate the number of equivalent current dipoles (ECDs) from instantaneous scalp EEG measurements, and that increase in the electrode number can improve the accuracy of estimation of the ECD number. For two ECDs, the best performance of estimation with 20% white noise were 85%, 92% and 94%, when 64, 128 and 256 electrodes are used, respectively. When there are 3 ECDs, the present results suggest that using 256 electrodes gave up to 82% estimation accuracy. The present simulation results also indicate that the accuracies of identification are similar when the minimum distance between dipoles is either 1 or 2 cm, which was used in the simulation. It was also found that the different penalty functions used in the information criterion method could have substantial influence on the estimation accuracy.Conclusions-The present method can estimate the number of ECDs from instantaneous scalp EEG distribution for up to three dipoles.Significance-The successful estimation of the number of ECDs will play an important role in expanding the applicability of dipole source localization to multiple sources.

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“…These efforts include equivalent dipole models (Scherg & Cramon, 1985;He et al, 1987;Cuffin, 1995), cortical current density (CCD) models (Dale and Sereno, 1993;Babiloni et al, 2003Babiloni et al, ,2005, distributed volume current density models (Pascual-Marqui et al, 1994a), and cortical potential distributions (Gevins et al, 1994;Nunez et al, 1994;He et al, 1999He et al, ,2001He et al, ,2002a). …”

Section: Introductionmentioning

confidence: 99%

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

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Objective-EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.Methods-Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L p -norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L p -norm (p equal to 1 and 1.5. When p = 2, the MNLS method is mathematically equivalent to the minimum L p -norm.), and L 1 -norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.Results-The CCD distributions estimated by the L p -norm and LORETA-L p methods were smoother when the p values were high. The LORETA based on the L 1 norm performed better than the LORETA-L 2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L p -norm (with p equal to 1), L 1 -norm (the Linear programming) and LORETA-L p (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L p -norm (p equal to 1.5 and 2.0), sLORETA, and the minimum L p -norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.Conclusions-The present experimental study suggests that L 1 -norm based algorithms provide better performance than L 2 and L 1.5 norm based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist.

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High resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods

Cited by 237 publications

References 21 publications

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

On the estimation of the number of dipole sources in EEG source localization

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

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