Identification of the temporal components of seizure onset in the scalp EEG.

BACKGROUND: The identification of the earliest indication of rhythmical oscillations and paroxysmal events associated with an epileptic seizure is paramount in identifying the location of the seizure onset in the scalp EEG. In this work, data-dependent filters are designed that can help reveal obscure activity at the onset of seizures in problematic EEGs. METHODS: Data-dependent filters were designed using temporal patterns common to selected segments from pre-ictal and ictal portions of the scalp EEG. Temporal patterns that accounted for more variance in the ictal segment than in the pre-ictal segment of the scalp EEG were used to form the filters. RESULTS: Application of the filters to the scalp EEG revealed temporal components in the seizure onset in the scalp recording that were not obvious in the unfiltered EEG. Examination of the filtered EEG enabled the onset of the seizure to be recognized earlier in the recording. The utility of the filters was confirmed qualitatively by comparing the scalp recording to the intracranial recording and quantitatively by calculating correlation coefficients between the scalp and intracranial recordings before and after filtering. CONCLUSION: The data-dependent approach to EEG filter design allows automatic detection of the basic frequencies present in the seizure onset. This approach is more effective than narrow band-pass filtering for eliminating artifactual and other interference that can obscure the onset of a seizure. Therefore, temporal-pattern filtering facilitates the identification of seizure onsets in challenging scalp EEGs.

Low-resolution electrical tomography of the brain during psychometrically matched verbal and spatial cognitive tasks.

EEGs were recorded from 75 normal, young, female subjects during psychometrically matched verbal (WF) and spatial (DL) cognitive tasks to elicit the differences in the electrical source distribution inside the brain. Recordings were obtained using 43 EEG and 3 guard electrodes then visually edited and spatially filtered to remove extracerebral artifacts. Twenty 1-sec artifact-free epochs were obtained and analyzed from 42 and 60 subjects during WF and DL respectively. Of these subjects, 20 were placed in a training set and the remainder into a test set. The baseline for the comparison of the two tasks was established by factoring the average cross-spectral matrices of the training-set EEGs, computed in the theta, alpha, and beta frequency bands into spatial patterns common to the two tasks. Only those spatial patterns that contributed to the correct classification of subjects in the test set were included in the source analysis. The source-current density distributions were obtained using the LORETA-KEY algorithm. The results show that the source-current density distribution is related to the putative functional activity in the brain in all three frequency bands. The electrical effects of the tasks are both most highly localized and lateralized in the theta band. The effects in the alpha and beta bands are much more generalized and are strongly lateralized only during one and the other of the tasks respectively. The conclusion is that WF is mainly a left central and bilateral frontal cerebral process while DL is mainly a right central and bilateral posterior cerebral process.

Discriminant analysis and equivalent source localization of the EEG related to cognitive functions.

Discriminant analysis and EEG source localization methods were employed to compare groups of normal subjects during different cognitive conditions using 43-channel EEG recordings in the alpha (8-13 Hz) frequency band. Recordings were obtained from 69 dextral females during 2 passive conditions, Eyes-Open and Eyes-Closed, and 2 active conditions, Word-Finding and Dot-Localization. The cross-spectral matrix between all of the electrode sites was used to characterize the EEGs obtained during each condition. The subjects were partitioned into training and test sets and quadratic discriminant functions were constructed from the training sets to classify the EEGs. The discriminant functions successfully classified both the training and test sets at rates approaching 80%. The classification was repeated using only the diagonal (power spectral) elements of the cross-spectral matrices in the discriminant functions and this approach was successful in discriminating between the EEGs from the passive cognitive conditions but failed to discriminate between the EEGs from the active conditions. Source localization using a modified MUSIC algorithm indicated that the centers of brain electrical activity that distinguished the Eyes-Closed condition from the Eyes-Open condition were located in the medial occipital and right frontal regions. Centers of electrical activity that distinguished the Word-Finding condition from the Dot-Localization condition were located in the right medial posterior and left temporal regions. Validation of the locations of the centers of activity was accomplished by repeating the classification procedures using the spatial patterns generated on the scalp by dipole current sources placed at these locations.

Trends in EEG source localization.

The concepts underlying the quantitative localization of the sources of the EEG inside the brain are reviewed along with the current and emerging approaches to the problem. The concepts mentioned include monopolar and dipolar source models and head models ranging from the spherical to the more realistic based on boundary and finite elements. The forward and inverse problems in electroencephalography are discussed, including the non-uniqueness of the inverse problem. The approaches to the solution of the inverse problem described include single and multiple time-slice localization, equivalent dipole localization and the weighted minimum norm. The multiple time-slice localization approach is highlighted as probably the best available at this time and is discussed in terms of the spatiotemporal model of the EEG. The effect of noise corruption, artifacts and the number of recording electrodes on the accuracy of source localization is also mentioned. It is suggested that the main appeal of the minimum norm is that it does not assume a model for the sources and provides an estimate of the current density everywhere in the three dimensional volume of the head.

EEG source localization: implementing the spatio-temporal decomposition approach.

OBJECTIVES: The spatio-temporal decomposition (STD) approach was used to localize the sources of simulated electroencephalograms (EEGs) to gain experience with the approach for analyzing real data. METHODS: The STD approach used is similar to the multiple signal classification method (MUSIC) in that it requires the signal subspace containing the sources of interest to be isolated in the EEG measurement space. It is different from MUSIC in that it allows more general methods of spatio-temporal decomposition to be used that may be better suited to the background EEG. RESULTS: If the EEG data matrix is not corrupted by noise, the STD approach can be used to locate multiple dipole sources of the EEG one at a time without a priori knowledge of the number of active sources in the signal space. In addition, the common-spatial-patterns method of spatio-temporal decomposition is superior to the eigenvector decomposition for localizing activity that is ictal in nature. CONCLUSIONS: The STD approach appears to be able to provide a means of localizing the equivalent dipole sources of realistic brain sources and that, even under difficult noise conditions and only 2 or 3 s of available EEG, the precision of the localization can be as low as a few mm.

Frequency domain discriminant analysis of the electroencephalogram.

A frequency domain generalization of the classical quadratic discriminant function was applied to the problem of classifying alpha-band multichannel electroencephalogram recordings in three task conditions. The data consisted of 41-channel recordings obtained in eyes closed, verbal, and spatial task conditions. Classifier performance was measured by deriving a decision rule from a training sample of 42 recordings and then applying the obtained rule to a test sample of 46 recordings. The proportion of correct classification was .93 in the training sample and .85 in the test sample. The classifier performed better when based on the complete cross-spectral matrix than when restricted to power spectrum variables. Classification based on a subset of 16 leads reduced the overall proportion of correct classification to .79 in the training sample and to .70 in the test sample.

Spatio-temporal decomposition of the EEG: a general approach to the isolation and localization of sources.

The principal-component method of source localization for the background EEG is generalized to arbitrary spatio-temporal decompositions. It is shown that as long as the spatial patterns of the decomposition span the same signal space as the principal spatial components, the computational process of attempting to localize the sources is the same. Decompositions other than the principal components are shown to be superior for the EEG in that they appear to enable individual sources to be better isolated. An example is given using the common spatial pattern decomposition and using a raw varimax rotation of a subset of the common spatial patterns. The results show that the principal component decomposition is almost ineffective for isolating spike and sharp wave activity in an EEG from a patient with epilepsy, that the common spatial pattern decomposition is significantly better and that the varimax rotation is better yet. That the varimax rotation is best is demonstrated by attempting to locate dipole sources inside the brain which account for the spike and sharp wave activity on the scalp. The question which remains is whether there exists some oblique rotation of the basis vectors of the EEG signal space which is optimal for isolating individual sources.

Principal-component localization of the sources of the background EEG.

A method, based on principal components for localizing the sources of the background EEG, is presented which overcomes the previous limitations of this approach. The spatiotemporal source model of the EEG is assumed to apply, and the method involves attempting to fit the spatial aspects of this general model with an optimal rotation of a subset of the principal components of a particular EEG. The method is shown to be equivalent to the subspace scanning method, a special case of the MUSIC algorithm, which enables multiple sources to be localized individually rather than all at once. The novel aspect of the new method is that it offers a way of selecting the relevant principal components for the localization problem. The relevant principal components are chosen by decomposing the EEG using spatial patterns common with a control EEG. These spatial patterns have the property that they account for maximally different proportions of the combined variances in the two EEG's. An example is given using a particular EEG from a neurologic patient. Components containing spike and sharp wave potentials are extracted, with respect to a standard EEG derived from 15 normal volunteers. Spike and sharp wave potentials are identified visually using the common spatial patterns decomposition and an EEG reconstructed from these components. Four dipole sources are fitted to the principal components of the reconstructed EEG and these source account for over 88% of the temporal variance present in that EEG.

Spatial patterns in the background EEG underlying mental disease in man.

The spatial patterns underlying differences in the background EEGs of schizophrenic, manic and depressed patients and a group of normal controls has been examined during the eyes open and eyes closed resting conditions and during 3 cognitive tasks. The method of principal-component analysis was used to extract spatial patterns which are common to the EEGs of 2 groups but which account for maximally different proportions of the combined variances. The common spatial patterns in all possible pairings of the groups were used to extract variance-related feature vectors from the individual EEG epochs in the 2 groups and the means of these vectors were subjected to statistical analyses. The results of these analyses indicate that there are significant differences in the EEGs from all 4 of the groups. The spatial patterns underlying the features which are significantly different in each comparison are shown graphically and used to suggest which brain regions might be implicated in each of the psychiatric conditions and how these are affected by the cognitive condition. The main results are that the EEGs in the schizophrenic group can be characterized by left-sided hyperactivity, in the depressed group by right-sided hyperactivity and in the manic group by bilateral hyperactivity and that these characteristics are best elicited by different cognitive states.

Systematic comparisons of interpolation techniques in topographic brain mapping.

The performance of one local interpolation technique, the nearest neighbors, and two global spline techniques, one planar and the other spherical, commonly used for topographic mapping of brain potential data has been quantitatively evaluated. The method of evaluation was one of cross-validation where the potential at each site in a 31-electrode full scalp recording montage is predicted by interpolation from the other sites. Errors between the measured potentials and those predicted by interpolation were quantified using 4 measures defined as inaccuracy, precision, bias and tolerance. The evaluation was applied to the background EEGs from 5 normal volunteers and from 4 patients with epilepsy, tumor or stroke. The results indicate that none of the interpolation techniques performed well and that for localized components in the EEG, the errors can increase almost without limit. Further, the global techniques performed significantly better than the local technique with 2 being the best order for the nearest-neighbor technique and 3 for the spline techniques. It is concluded that interpolation should not be used with electrode densities of the order of that provided by the international 10-20 system neither to increase the spatial resolution of the electroencephalogram nor in more sophisticated analysis techniques in quantitative EEG for estimates such as the radial-current density.

Image restoration in computed tomography: the spatially invariant point spread function.

Image restoration to deblur smoothing caused by the finite-size X-ray beam profile for a simulated computed tomography (CT) system is presented. Three simple image restoration methods are compared when the point-spread-function (PSF) is spatially invariant. In the first restoration method, an iterative least squares solution, regularized with the image norm and constrained by the boundary of the object, is obtained from the projection data. In the second method, a Wiener filter, designed using the power spectrum of CT noise, is applied to the reconstructed CT image. The third method obtains a weighted least-squares solution, by iteration, from the reconstructed CT image; the solution is regularized with the weighted image norm. Restored images were compared with the image obtained using filtered backprojection method. Differences between these images were evaluated qualitatively.

Image restoration in computed tomography: estimation of the spatially variant point spread function.

An indirect method for determining the point-spread-function (PSF) in computed tomography (CT) is described. Unlike experimental techniques in which a resolution phantom is scanned to obtain the system PSF, the approach estimates the parameters of a model which describes the two-dimensional X-ray beam profile at each point as a convolution of the appropriately scaled focal spot intensity and detector sensitivity distributions. The model was validated by experimental measurement of the focal spot intensity distribution. Using known X-ray beam profiles, the PSF of a CT scanner can be derived by simulating the data collection process and applying conventional image reconstruction techniques. Visual comparison of directly measured and computed PSFs reveals an asymmetry resulting from misregistration of the phantom wires and the image grid.

Image restoration in computed tomography: restoration of experimental CT images.

It is pointed out that to restore experimental computed tomography (CT) images which have been blurred by a spatially variant point spread function (PSF), a quadrant symmetry method which simultaneously optimizes storage requirements for the estimated PSFs and computational speed is used. The quadrant symmetry approach required less than 0.579 Megawords of storage for 9x9 pixel spatially variant PSFs in a 256x256 pixel image, allowing image restoration in Cartesian coordinates. A locally adaptive restoration method formulated using a noise prewhitening filter derived from the measured noise power spectrum, the conjugate gradient method was used to obtain a numerical solution. Restored images for a resolution phantom and for a cadaver femur are presented.

The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG.

A method is described which seems to be effective for extracting the abnormal components from the clinical EEG. The approach involves the use of a set a spatial patterns which are common to recorded and 'normal' EEGs and which can account for maximally different proportions of the combined variances in both EEGs. These spatial factors are used to decompose the EEG into orthogonal temporal wave forms which can be judged by the expert electroencephalographer to be abnormal, normal or of artifactual origin. The original EEG is then reconstructed using only the abnormal components and principal component analysis is used to present the spatial topography of the abnormal components. The effectiveness of the method is discussed along with its value for localization of abnormal sources. It is suggested, in conclusion, that the approach described may be optimal for interpretation of the clinical EEG since it allows what is best in terms of quantitative analysis of the EEG to be combined with the best that is available in terms of expert qualitative analysis.

Quantitative EEG studies of pedophilia.

Ninety six pedophiles, whose sexual orientation was confirmed by phallometric response to sexual stimuli, were investigated with quantitative EEG and compared to age- and sex-matched healthy controls. The EEG analysis showed a pattern of increased frontal delta, theta and alpha power (especially during verbal processing) and a pattern of reduced interhemispheric and increased intrahemispheric-interhemispheric coherence, right and left (only during verbal processing), an effect that was restricted to those who showed maximal erotic arousal for sexual partners aged 6-12 years. These findings will be discussed in the context of recent studies which suggest that sexual deviations in the male relate to altered dominant hemispheric functions with disruption of frontal interhemispheric relationships.

Neurophysiological and neuropsychological study of two cases of multiple personality syndrome and comparison with chronic hysteria.

Two cases of multiple personality were studied neurophysiologically and neuropsychologically. Bilateral frontal (Right greater than Left) and left temporal dysfunction was present in both cases, on neuropsychological indicators. Both cases on EEG analysis, were in a state of relative left hemisphere activation, across all cerebral regions and task conditions. The one case who was cured with hypnotherapy, after recovery showed normal left hemisphere functions neuropsychologically but remained in a state of relative left hemisphere activation electrophysiologically. This is in contrast to women with chronic hysteria who exhibit relative right hemisphere activation in all regions and across all conditions. Both patients were unmedicated throughout. A neurophysiological model to account for these findings is presented.

Spatial patterns underlying population differences in the background EEG.

A method is described which can be used to extract common spatial patterns underlying the EEGs from two human populations. These spatial patterns account, in the least-squares sense, maximally for the variance in the EEGs from one population and minimally for the variance in the other population and therefore would seem to be optimal for quantitatively discriminating between the individual EEGs in the two populations. By using this method, it is suggested that the problems associated with the more common approach to discriminating EEGs, significance probability mapping, can be avoided. The method is tested using EEGs from a population of normal subjects and using the EEGs from a population of patients with neurologic disorders. The results in most cases are excellent and the misclassification which occurs in some cases is attributed to the nonhomogeneity of the patient population particularly. The advantages of the method for feature selection, for automatically classifying the clinical EEG, and with respect to the reference-free nature of the selected features are discussed.

A spatial power spectrum analysis of the electroencephalogram.

The method of spatial power-spectrum analysis has been applied to measurements of the distribution of rms alpha-band potential on the scalp. Data was recorded using the 31-Electrode System and spatial power-spectrum estimates (PSEs) were obtained from Mercator projections of the potential interpolated using the triangular method. PSEs were calculated using the Lim and Malik algorithm for maximum-entropy power-spectrum estimation. In order to investigate the utility of spatial power-spectrum analysis, PSEs were obtained from subjects in two conditions; resting with eyes closed (EC) and resting with eyes open and fixed on a single point (EO). A stepwise discriminant analysis was performed with features from the PSEs and the resultant discriminant function was applied to data not considered in the formulation of the function. Over 92% of test data was correctly classified. The features used in the discriminant function identify spatial waves which are most useful in separating data. The results demonstrate that waves oriented along front-back and right-left lines are most important in separating data into EC and EO groups.

Computed radial-current topography of the brain: patterns associated with the normal and abnormal EEG.

We describe a method for producing estimates of radial-current topography underlying the background EEG. This method is based on the application of the laplacian operator to potentials measured on the scalp using a 31-electrode recording array. The laplacian is applied analytically to a potential surface obtained by bicubic-spline interpolation of the measurements at the electrode sites. The results obtained when the method was applied to the alpha rhythm recorded from a normal volunteer and to the slow wave activity recorded from a neurologic patient are presented. The alpha rhythm is associated with areas of strong radial-current activity in the occipital regions (although dominantly right); for the slow rhythm the activity appears in the medial-frontal region. The radial-current topography for the alpha rhythm suggests rotating dipole generators in the occipital lobes whereas it is suggestive of a radially oriented dipole in the case of the delta activity. Discussion is focused upon the apparent advantages of radial-current topography for localizing brain electrical activity, upon the strengths and weaknesses of the method, and upon the observation that the topography of radial current activity obtained would have been difficult to predict from a visual examination of raw EEG traces alone.

Topographic mapping of the EEG: an examination of accuracy and precision.

The accuracy and precision of topographic maps depicting scalp potentials and scalp potentials squared have been examined. Electrode placement was that specified by the International 10-20 System and the methods of interpolation bilinear and bicubic spines. The results indicate that, for these interpolation methods, the maximum error expected between the measured scalp quantities and those predicted by interpolation is positively correlated to the root-mean-square value of the measured quantity. Both interpolation methods produce accurate estimates of the interelectrode quantities. Both methods produce precise estimates of the scalp potential in the delta, theta and alpha frequency bands but only poor estimates in the beta band. The precision of the estimates of the scalp potentials squared is poor in all frequency bands. This result indicates that another look at the now common practice of topographically mapping the power-spectral components of the EEG is in order. In general, the bilinear and bicubic spline methods of interpolation perform about equally. This result is used to suggest that because of its additional computational complexity, use of the bicubic method for potential mapping may not be warranted. Advantages of the bicubic method, particularly in radial-current mapping, are however discussed.