Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head

Modeling in magnetoencephalography (MEG) and electroencephalography (EEG) requires knowledge of the in vivo tissue resistivities of the head. The aim of this paper is to examine the influence of tissue resistivity changes on the neuromagnetic field and the electric scalp potential. A high-resolution finite element method (FEM) model (452,162 elements, 2-mm resolution) of the human head with 13 different tissue types is employed for this purpose. Our main finding was that the magnetic fields are sensitive to changes in the tissue resistivity in the vicinity of the source. In comparison, the electric surface potentials are sensitive to changes in the tissue resistivity in the vicinity of the source and in the vicinity of the position of the electrodes. The magnitude (strength) of magnetic fields and electric surface potentials is strongly influenced by tissue resistivity changes, while the topography is not as strongly influenced. Therefore, an accurate modeling of magnetic field and electric potential strength requires accurate knowledge of tissue resistivities, while for source localization procedures this knowledge might not be a necessity.

The influence of brain tissue anisotropy on human EEG and MEG

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.

Dipole models for the EEG and MEG

The current dipole is a widely used source model in forward and inverse electroencephalography and magnetoencephalography applications. Analytic solutions to the governing field equations have been developed for several approximations of the human head using ideal dipoles as the source model. Numeric approaches such as the finite-element and finite-difference methods have become popular because they allow the use of anatomically realistic head models and the increased computational power that they require has become readily available. Although numeric methods can represent more realistic domains, the sources in such models are an approximation of the ideal dipole. In this paper, we examine several methods for representing dipole sources in finite-element models and compare the resulting surface potentials and external magnetic field with those obtained from analytic solutions using ideal dipoles.

Influence of head models on EEG simulations and inverse source localizations

Background

The structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models.

Methods

Four finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissue-type model, (3) the Model 3 was derived from the Model 1 in which the conductivities of gray matter and CSF were set equal to the white matter, i.e., a nine tissue-type model, (4) the Model 4 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. How model complexity influences the EEG source localizations was also studied with the above four finite element models of the head. The lead fields and scalp potentials due to dipolar sources in the motor cortex were computed for all four models. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. The inverse analysis was performed by adding uncorrelated Gaussian noise to the scalp potentials to achieve a signal to noise ratio (SNR) of -10 to 30 dB. The Model 1 was used as a reference model.

Results

The reference model, as expected, performed the best. The Model 3, which did not have the CSF layer, performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The scalp potentials were also most affected by the lack of CSF geometry in the Model 3. The MLEs for the Model 4 were also larger than the Model 1 and 2. The Model 4 and the Model 3 had similar MLEs in the SNR range of -10 dB to 0 dB. However, in the SNR range of 5 dB to 30 dB, the Model 4 has lower MLEs as compared with the Model 3.

Discussion

These results indicate that the complexity of head models strongly influences the scalp potentials and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces. The CSF layer plays an important role in modifying the scalp potentials and also influences the inverse source localizations. In summary, for best results one needs to have highly heterogeneous models of the head for accurate simulations of scalp potentials and for inverse source localizations.

Role of soft bone, CSF and gray matter in EEG simulations

Effects of soft skull bone, cerebrospinal fluid (CSF) and gray matter on scalp potentials were examined with highly heterogeneous finite element models of an adult male subject. These models were constructed from segmented T1 weighted magnetic resonance images. Models had voxel resolutions of 1x1x3.2 mm with a total of about 1.5 million voxels. The scalp potentials, due to a dipolar source in the motor cortex area, were computed with an adaptive finite element solver. It was found that the scalp potentials were significantly affected by the soft bone, CSF and gray matter tissue boundaries in the models.

On the influence of volume currents and extended sources on neuromagnetic fields: a simulation study

The influence of volume currents on the magnetic field is an important question in magnetoencephalography since the spherical volume conductor is still widely used for source localization. In theory, the magnetic field of a radial dipole in a homogeneous sphere is zero. In realistic models of the head, the field is suppressed when compared with a tangential dipole. To determine the influence of the volume currents, this suppression ratio (magnetic field of the radial dipole divided by the field of the tangential dipole) needs to be quantified. Large-scale finite element method models of the human head and the rabbit head were constructed and the suppression ratio was computed. The computed suppression value of 0.28 in the rabbit head was similar to the previously measured experimental value. In the human head, an average suppression ratio of 0.19 +/- 0.07 was found for different regions and depths in the gray matter. It was found that the computed magnetic field of radial sources varied significantly with the conductivities of the surrounding tissues where the dipole was located. We also modeled the magnetic field of an epileptic interictal spike in a finite element model of the rabbit head with a single dipole and with extended sources of varying length (1-8 mm). The extended source models developed were based on invasive measurements of an interictal spike within the rabbit brain. The field patterns of the small (1-2 mm) extended sources were similar to a single dipolar source and begin to deviate significantly from a dipolar field for the larger extended sources (6-8 mm).

Low-level, magnetic-field-induced growth modification of Bacillus subtilis

Experimental studies showed an increase in the growth of Bacillus subtilis mutant strain FJ7 above controls by exposing the bacterial culture to 800-Hz or 1-KHz magnetic fields with a 2-s-on/2-s-off period. The magnetic field strength was between 0.8 and 2.5 mT. Light microscopy and scanning electron microscopy demonstrated the morphology of controls to grow in a macrofiber of right-handed helix formation. In contrast, the field-exposed group showed little to no cohesion; the cells appeared to be homogeneously distributed throughout the sample. These results suggest that growth patterns of Bacillus subtilis can be altered as a result of magnetic-field-induced effects.

Synchronization analysis of the uterine magnetic activity during contractions

Background

Our objective was to quantify and compare the extent of synchronization of the spatial-temporal myometrial activity over the human uterus before and during a contraction using transabdominal magnetomyographic (MMG) recordings. Synchronization can be an important indicator for the quantification of uterine contractions.

Methods

The spatialtermporal myometrial activity recordings were performed using a 151-channel noninvasive magnetic sensor system called SARA. This device covers the entire pregnant abdomen and records the magnetic field corresponding to the electrical activity generated in the uterine myometrium. The data was collected at 250 samples/sec and was resampled with 25 samples/sec and then filtered in the band of 0.1–0.2 Hz to study the primary magnetic activity of the uterus related to contractions. The synchronization between a channel pair was computed. It was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The analytic phase was computed after taking Hilbert transform of the magnetic field data. The process was applied on the pairs of magnetic field traces (240 sec length) with a stepping window of 20 sec duration which is long enough to cover two cycle of the lowest frequency of interest (0.1 Hz). The analysis was repeated by stepping the window at 10 sec intervals. The spatial patterns of the synchronization indices covering the anterior transabdominal area were computed. For this, regional coil-pairs were used. For a given coil, the coil pairs were constructed with the surrounding six coils. The synchronization indices were computed for each coil pair, averaged over the 21 coil-pairs and then assigned as the synchronization index to that particular coil. This procedure was tested on six pregnant subjects at the gestational age between 29 and 40 weeks admitted to the hospital for contractions. The RMS magnetic field for each coil was also computed.

Results

The results show that the spatial patterns of the synchronization indices change and follow the periodic pattern of the uterine contraction cycle. Spatial patterns of synchronization indices and the RMS magnetic fields show similarities in few window frames and also show large differences in few other windows. For six subjects, the average synchronization indices were: 0.346 ± 0.068 for the quiescent baseline period and 0.545 ± 0.022 at the peak of the contraction.

Discussion

These results show that synchronization indices and their spatial distributions depict uterine contractions and relaxations.

Changes in scalp potentials and spatial smoothing effects of inclusion of dura layer in human head models for EEG simulations

The dura layer which covers the brain is less conductive than the CSF (cerebrospinal fluid) and also more conductive than the skull bone. This could significantly influence the flow of volume currents from cortex to the scalp surface which will also change the magnitude and spatial profiles of scalp potentials. This was examined with a 3-D finite element method (FEM) model of an adult subject constructed from 192 segmented axial magnetic resonance (MR) slices with 256×256 pixel resolution. The voxel resolution was 1×1×1 mm. The model included the dura layer. In addition, other major tissues were also identified. The electrical conductivities of various tissues were obtained from the literature. The conductivities of dura and CSF were 0.001 S/m and 0.06 S/m, respectively. The electrical activity of the cortex was represented by 144,000 distributed dipolar sources with orientations normal to the local cortical surface. The dipolar intensity was in the range of 0.0-0.4 mA meter with a uniform random distribution. Scalp potentials were simulated for two head models with an adaptive finite element solver. One model had the dura layer and in the other model, dura layer was replaced with the CSF. Spatial contour plots of potentials on the cortical surface, dural surface and the scalp surface were made. With the inclusion of the dura layer, scalp potentials decrease by about 20%. The contours of gyri and sulci structures were visible in the spatial profiles of the cortical potentials which were smoothed out on the dural surface and were not visible on the scalp surface. These results suggest that dura layer should be included for an accurate modeling of scalp and cortical potentials.

Inhibition of growth rate of Escherichia coli induced by extremely low-frequency weak magnetic fields

Cultures of Escherichia coli kept at 0 degree C in a phosphate buffer solution were exposed to a sinusoidal weak 60- or 600-Hz magnetic field of strength 2 X 10(-3) Tesla. A decrease of more than 40% in bacterial count was observed after a 60-h exposure to the magnetic field. Electron micrographs of exposed bacteria show ruptured cell walls, possibly due to the breaking away of flagella under the influence of the sinusoidally varying electromotive force.

CT and MRI Assessment and Characterization Using Segmentation and 3D Modeling Techniques: Applications to Muscle, Bone and Brain

This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain. This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functional electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by developing detailed 3D representations of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information with the equivalent electrical dipole sources as an input into the model.

Combination of capillary electrophoresis and matrix-assisted laser desorption ionization mass spectrometry for glycosylation analysis of a human monoclonal anti-Rhesus(D) antibody

Characterization of a human anti-Rhesus(D) monoclonal antibody, developed for the treatment of Rh(D) haemolytic disease of the newborn, was performed. Capillary electrophoresis (CE) has been employed for peptide mapping of the IgG heavy chain and glycopeptide identification. The combination of the high resolution and low solvent consumption of CE and the ultrasensitive detection and precise identification properties of mass spectrometry led to a complete glycosylation analysis of the protein. Glycopeptides were easily isolated from a single injection in a 100 microns i.d. capillary of the preparative CE system and collected for molecular mass determination using matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). The off-line CE-MS characterization revealed the presence of different oligosaccharides linked to the unique N297-S-T glycosylation site of the IgG heavy chain. The differences between calculated and experimental masses of the glycopeptides suggested the presence of a fucosylated biantennary structure containing one or two galactose units as major oligosaccharide, together with similar species bearing a bisecting N-acetylglucosamine. CE conditions were optimized to allow the MS identification of sialylated forms.

Influence of head models on neuromagnetic fields and inverse source localizations

Background

The magnetoencephalograms (MEGs) are mainly due to the source currents. However, there is a significant contribution to MEGs from the volume currents. The structure of the anatomical surfaces, e.g., gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the MEGs and the inverse source localizations. This was examined in detail with three different human head models.

Methods

Three finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissuetype model, (3) the Model 3 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. The lead fields and MEGs due to dipolar sources in the motor cortex were computed for all three models. The dipolar sources were oriented normal to the cortical surface and had a dipole moment of 100 μA meter. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. A set of 100 trial inverse runs was made covering the 3 cm cube motor cortex area in a random fashion. The Model 1 was used as a reference model.

Results

The reference model (Model 1), as expected, performed best in localizing the sources in the motor cortex area. The Model 3 performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The contour plots of the magnetic fields on top of the head were also different for all three models. The magnetic fields due to source currents were larger in magnitude as compared to the magnetic fields of volume currents.

Discussion

These results indicate that the complexity of head models strongly influences the MEGs and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces.

Similarities between simulated spatial spectra of scalp EEG, MEG and structural MRI

Electrical dipoles oriented perpendicular to the cortical surface are the primary source of the scalp EEGs and MEGs. Thus, in particular, gyri and sulci structures on the cortical surface have a definite possibility to influence the EEGs and MEGs. This was examined by comparing the spatial power spectral density (PSD) of the upper portion of the human cortex in MRI slices to that of simulated scalp EEGs and MEGs. The electrical activity was modeled with 2,650 dipolar sources oriented normal to the local cortical surface. The resulting scalp potentials were calculated with a finite element model of the head constructed from 51 segmented sagittal MR images. The PSD was computed after taking the fast Fourier transform of scalp potentials. The PSD of the cortical contour in each slice was also computed. The PSD was then averaged over all the slices. This was done for sagittal and coronal view both. The PSD of EEG and MEG showed two broad peaks, one from 0.05 to 0.22 cycles/cm (wavelength 20–4.545 cm) and the other from 0.22 to 1.2 cycles/cm (wavelength 4.545–0.834 cm). The PSD of the cortex showed a broad peak from 0.08 to 0.32 cycles/cm (wavelength 12.5–3.125 cm) and other two peaks within the range of 0.32 to 0.9 cycles/cm (wavelength 3.125–1.11 cm). These peaks are definitely due to the gyri structures and associated larger patterns on the cortical surface. Smaller peaks in the range of 1–3 cycles/cm were also observed which are possibly due to sulci structures. These results suggest that the spatial information was present in the EEG and MEG at the spatial frequencies of gyri. This also implies that the practical Nyquist frequency for sampling scalp EEGs should be 3.0 cycles/cm and an optimal interelectrode spacing of about 3 mm is needed for extraction of cortical patterns from scalp EEGs in humans.

On the contribution of volume currents to the total magnetic field resulting from the heart excitation process: a simulation study

Data from a simulation study of volume current contribution to the total magnetic field produced in the heart excitation process is presented. Contributions from different tissue types are analyzed and effects of torso size are studied. A high resolution finite element model of an adult male torso composed of 19 tissue types is used. It has detailed description of tissue geometries and therefore is well suited for analyzing the contribution of the primary and secondary currents to the magnetic field. The computed results show major contribution of volume currents from blood, myocardium, and lungs and less significant contribution from liver, muscle, and other tissues. The contribution to the volume currents from the blood in the ventricles was highest. These simulations suggest that contribution to the total magnetic field due to volume currents flowing in tissues other than blood could be accounted for by simply multiplying the total field values by a constant. Values of these multipliers would be based on the tissue type and time in the excitation cycle. Effects of torso size on the computed magnetic fields are also evaluated. Our data shows that a torso extending approximately 3 cm above and below the heart produces field patterns similar to a larger torso model extending from top of guts to the bottom of neck. Thus a shorter torso model would be sufficient for cardiac magnetic field analysis. These results are of interest for future modeling of magnetocardiograms and solving the inverse problem.

MCG simulations with a realistic heart-torso model

Magnetocardiograms (MCG's) simulated with a high-resolution heart-torso model of an adult subject were compared with measured MCG's acquired from the same individual. An exact match of the measured and simulated MCG's was not found due to the uncertainties in tissue conductivities and cardiac source positions. However, general features of the measured MCG's were reasonably represented by the simulated data for most, but not all of the channels. This suggests that the model accounts for the most important mechanisms underlying the genesis of MCG's and may be useful for cardiac magnetic field modeling under normal and diseased states. MCG's were simulated with a realistic finite-element heart-torso model constructed from segmented magnetic resonance images with 19 different tissue types identified. A finite-element model was developed from the segmented images. The model consists of 2.51 million brick-shaped elements and 2.58 million nodes, and has a voxel resolution of 1.56 x 1.56 x 3 mm. Current distributions inside the torso and the magnetic fields and MCG's at the gradiometer coil locations were computed. MCG's were measured with a Philips twin Dewar first-order gradiometer SQUID-system consisting of 31 channels in one tank and 19 channels in the other.

Effect of myocardial anisotropy on the torso current flow patterns, potentials and magnetic fields

The effects of myocardial anisotropy on the torso current flow patterns, voltage and the magnetic field were examined using an anatomically realistic torso model of an adult male subject. A finite element model of the torso was built with 19 major tissue types identified. The myocardial fibre orientation in the heart wall was included with a voxel resolution of 0.078 x 0.078 x 0.3 cm. The fibre orientations from the canine heart which are available in the literature were mapped to our adult male subject's human heart using deformable mapping techniques. The current and potential distribution in the whole torso were computed using an idealized dipolar source of +/-1.0 V in the middle of the septum of the heart wall as a boundary condition. An adaptive finite element solver was used. Two cases were studied. In one case the myocardium was isotropic and in the other it was anisotropic. It was found that the current density distribution shows a very noticeable difference between the isotropic and anisotropic myocardium. The resultant magnetic field in front of the torso was computed using the Biot-Savart law. It was found that the magnetic field profile was slightly affected by the myocardial anisotropy. The potential on the torso surface also shows noticeable changes due to the myocardial anisotropy.

The influence of local tissue conductivity changes on the magnetoencephalogram and the electroencephalogram

We examined the influence of local tissue conductivity changes in the vicinity of a dipolar source on the neuromagnetic field and the electric scalp potential using a high resolution finite element method model of the human head. We found that the topology of both the electric scalp potential and the neuromagnetic field (and consequently dipole localization) is influenced significantly by conductivity changes only in voxels adjacent to the source. Conductivity changes in these voxels yield a greater change in the amplitude of the magnetic field (and consequently in the dipole strength) than in the amplitude of the electric potential.

Simulation studies of biomagnetic computed tomography

The reconstruction of planar and three-dimensional current distributions from measured biomagnetic signals is a new field of research, known as biomagnetic computed tomography. This noninvasive imaging technique promises to provide precise, millimeter-sized resolution images of the electrical currents in tissues or organs. We performed simulation studies on phantom models of electrical sources. As a first step towards the development of an imaging algorithm, we addressed a simplified problem to identify the shape and direction of current flow in a planar surface. The problem was formulated by identifying a space in which the image was to be reconstructed. The space was segmented into a grid. Each grid space represented a current element. The magnetic field at a sampling point due to the current elements was computed using the Biot-Savart law. Since there were many more current elements than sample points, the problem was undetermined and had an uncountable number of solutions. The projection theorem was used to define an analytic solution for the magnitude and orientation of the current elements in the grid space. The solution required the inversion of large matrices in double precision. Such arrays were preprocessed on a mainframe computer, which permitted them to be rendered on any workstation. The accuracy of the image was determined by comparing it with the known location of the sources. Our results show that shape of the filamentary current flow can be imaged with our techniques. The resolution of images based on the sampling of the field, number of voxels in the reconstruction space, and noise is also analyzed.

Efficient electromagnetic source imaging with adaptive standardized LORETA/FOCUSS

Functional brain imaging and source localization based on the scalp's potential field require a solution to an ill-posed inverse problem with many solutions. This makes it necessary to incorporate a priori knowledge in order to select a particular solution. A computational challenge for some subject-specific head models is that many inverse algorithms require a comprehensive sampling of the candidate source space at the desired resolution. In this study, we present an algorithm that can accurately reconstruct details of localized source activity from a sparse sampling of the candidate source space. Forward computations are minimized through an adaptive procedure that increases source resolution as the spatial extent is reduced. With this algorithm, we were able to compute inverses using only 6% to 11% of the full resolution lead-field, with a localization accuracy that was not significantly different than an exhaustive search through a fully-sampled source space. The technique is, therefore, applicable for use with anatomically-realistic, subject-specific forward models for applications with spatially concentrated source activity.

The effect of volume currents due to myocardial anisotropy on body surface potentials

Changes in anterior and posterior body surface potential maps (BSPMs) due to myocardial anisotropy were examined using a highly heterogeneous finite element model of an adult male subject constructed from segmented magnetic resonance images. A total of 23 different tissue types were identified in the whole torso. The myocardial fibre orientations in the human heart wall were mapped from the fibre orientations of a canine heart which are available in the literature using deformable mapping techniques. The current and potential distributions in the whole torso were computed using dipolar sources in the septum, apical area, left ventricular wall or right ventricular wall. For each dipole x, y, z orientations were studied. An adaptive finite element solver was used to compute currents and potential distributions in the whole torso with an element size of 0.78 x 0.78 x 3 mm in the myocardium and larger elements in other parts of the torso. For each dipole position two cases were studied. In one case the myocardium was isotropic and in the other it was anisotropic. It was found that BSPMs showed a very notable difference between the isotropic and the anisotropic myocardium for all dipole positions with the largest difference for the apical dipoles. The correlation coefficients for the BSPMs between the isotropic and anisotropic cases ranged from 0.83 for an apical dipole to 0.99 for an RV wall dipole. These results suggest that myocardial fibre anisotropy plays an important role in determining the body surface potentials.

Power spectral density changes and language lateralization during covert object naming tasks measured with high-density EEG recordings

Our objective was to study changes in EEG time-domain power spectral density (PSDt) and localization of language areas during covert object naming tasks in human subjects with epilepsy. EEG data for subjects with epilepsy were acquired during the covert object naming tasks using a net of 256 electrodes. The trials required each subject to provide the names of common objects presented every 4 seconds on slides. Each trial comprised the 1.0 second before and 3.0 seconds after initial object presentation. PSDt values at baseline and during tasks were calculated in the theta, alpha, beta, low gamma, and high gamma bands. The spatial contour plots reveal that PSDt values during object naming were 10-20% higher than the baseline values for different bands. Language was lateralized to left frontal or temporal areas. In all cases, the Wada test disclosed language lateralization to the left hemisphere as well.