Age-related features of the resting and P300 auditory evoked responses using the dipole localization method and cortical imaging technique

Sidman, Robert D.; Ford, Martin R.; Ramsey, Gloria; Schlichting, Christine L

doi:10.1016/0165-0270(90)90078-t

Cited by 49 publications

(31 citation statements)

References 18 publications

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“…The FEM-based CPI (FEM-CPI) inverse solver in the present study includes two steps, which is also known as indirect CPI as reported previously in the spherical head model (Sidman et al, 1990;He et al, 1996;Wang & He, 1998) and in BE models (Babiloni et al, 1997). The first step is to estimate the current dipole strength distribution on an equivalent dipole layer (EDL), which has fixed number of dipoles with fixed locations and orientations, and has been studied to represent the current dipole distribution in a 3-dimensional (3D) volume (Sidman et al, 1990;He et al, 1996;Babiloni et al, 1997;Wang & He, 1998).…”

Section: Fem-cpi Inverse Solvermentioning

confidence: 99%

“…The first step is to estimate the current dipole strength distribution on an equivalent dipole layer (EDL), which has fixed number of dipoles with fixed locations and orientations, and has been studied to represent the current dipole distribution in a 3-dimensional (3D) volume (Sidman et al, 1990;He et al, 1996;Babiloni et al, 1997;Wang & He, 1998). The EDL located just inside of the cortical surface (8 mm below the epicortical surface) in the FE model (see Section II.3 for details) and shared the same geometric shape with the epicortical surface.…”

Section: Fem-cpi Inverse Solvermentioning

confidence: 99%

“…While the scalp electroencephalogram (EEG) has the advantage of unsurpassed millisecond temporal resolution necessary for resolving dynamic neural processes, its application to mapping spatial distribution of brain electrical activity is limited by its poor spatial resolution due to the blurring effect of the head volume conductor, especially the low-conductivity skull layer (Nunez, 1981;He, 2004He, , 2005. Tremendous effort has been made to enhance the spatial resolution of the conventional EEG by solving the so-called EEG inverse problem, which attempts to overcome the head volume conductor effect (Scherg & Von Cramon, 1985;He et al, 1987He et al, , 1996He et al, , 1999He et al, , 2001He et al, , 2002Cohen et al, 1990;Sidman et al, 1990;Dale & Sereno, 1993;Le & Gevins, 1993;Ebersole et al, 1994;Gevins et al, 1994;Nunez et al, 1994;Babiloni et al, 1997Babiloni et al, , 2003Fuchs et al, 1999;Michel et al, 1999;Ollikainen et al, 2001;Zhang et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…An early attempt to reconstruct the cortical potentials used an intermediate hemisphere equivalent dipole layer to generate an inward harmonic potential function in a homogeneous head volume conductor model (Sidman et al, 1990). The inverse procedure was used to estimate the equivalent dipole distribution from the scalp EEG, and then the cortical potentials were reconstructed by solving the forward problem from the estimated equivalent dipole layer to the cortical potential.…”

Section: Introductionmentioning

confidence: 99%

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A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

et al. 2006

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In the present study, we have validated the cortical potential imaging (CPI) technique for estimating cortical potentials from scalp EEG using simultaneously recorded electrocorticogram (ECoG) in the presence of strong local inhomogeneity, i.e. Silastic ECoG grid(s). The finite element method (FEM) was used to model the realistic post-operative head volume conductor, which includes the scalp, skull, cerebrospinal fluid (CSF) and brain, as well as the Silastic ECoG grid(s) implanted during the surgical evaluation in epilepsy patients, from the co-registered magnetic resonance (MR) and computer tomography (CT) images. A series of computer simulations were conducted to evaluate the present FEM-based CPI technique, and to assess the effect of the Silastic ECoG grid on the scalp EEG forward solutions. The present simulation results show that the Silastic ECoG grid has substantial influence on the scalp potential forward solution due to the distortion of current pathways in the presence of the extremely low conductivity materials. On the other hand, its influence on the estimated cortical potential distribution is much less than that on the scalp potential distribution. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the feasibility of estimating the cortical potentials from the scalp EEG with the implanted Silastic ECoG gird(s), in both computer simulations and in human experimentation. In an epilepsy patient undergoing surgical evaluation, the cortical potentials were reconstructed from the simultaneously recorded scalp EEG, in which main features of spatial patterns during interictal spike were preserved and over 0.75 correlation coefficient value was obtained between the recorded and estimated cortical potentials. The FEM-based CPI technique provides a means of connecting the simultaneous recorded ECoG and the scalp EEG, and promises to become an effective tool to evaluate and validate CPI techniques using clinic data.

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Section: Fem-cpi Inverse Solvermentioning

confidence: 99%

Section: Fem-cpi Inverse Solvermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

et al. 2006

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“…In the inhomogeneous spherical source-conductor model, the radii of the brain, the skull, and the scalp spheres were taken as 0.87, 0.92, and 1.0, respectively [6], [11], [26]. The normalized conductivity of the scalp and the brain was taken as σ 0 = 1.0, and that of the skull as σ s = 0.0125.…”

Section: Simulationmentioning

confidence: 99%

Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging

Hori

Sunaga

Watanabe

2010

IEICE Trans. Inf. & Syst.

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SUMMARYWe investigated suitable spatial inverse filters for cortical dipole imaging from the scalp electroencephalogram (EEG). The effects of incorporating statistical information of signal and noise into inverse procedures were examined by computer simulations and experimental studies. The parametric projection filter (PPF) and parametric Wiener filter (PWF) were applied to an inhomogeneous three-sphere volume conductor head model. The noise covariance matrix was estimated by applying independent component analysis (ICA) to scalp potentials. The present simulation results suggest that the PPF and the PWF provided excellent performance when the noise covariance was estimated from the differential noise between EEG and the separated signal using ICA and the signal covariance was estimated from the separated signal. Moreover, the spatial resolution of the cortical dipole imaging was improved while the influence of noise was suppressed by including the differential noise at the instant of the imaging and by adjusting the duration of noise sample according to the signal to noise ratio. We applied the proposed imaging technique to human experimental data of visual evoked potential and obtained reasonable results that coincide to physiological knowledge.

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Electroencephalography (EEG): Inverse Problems

Hori

Babiloni³

2006

Wiley Encyclopedia of Biomedical Engineering

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Age-related features of the resting and P300 auditory evoked responses using the dipole localization method and cortical imaging technique

Cited by 49 publications

References 18 publications

A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging

Electroencephalography (EEG): Inverse Problems

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