Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

BACKGROUND

Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters.

NEW METHOD

We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter.

RESULTS

The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal.

COMPARISON WITH EXISTING METHODS

Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR.

CONCLUSIONS

Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

Back-Projection Cortical Potential Imaging: Theory and Results

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better neurological inferences from the EEG.

Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review

Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

The use of current source density as electrophysiological correlates in neuropsychiatric disorders: A review of human studies

The use of current source density (CSD), the Laplacian of the scalp surface voltage, to map the electrical activity of the brain is a powerful method in studies of cognitive and affective phenomena. During the last few decades, mapping of CSD has been successfully applied to characterize several neuropsychiatric conditions such as alcoholism, schizophrenia, depression, anxiety disorders, childhood/developmental disorders, and neurological conditions (i.e., epilepsy and brain lesions) using electrophysiological data from resting state and during cognitive performance. The use of CSD and Laplacian measures has proven effective in elucidating topographic and activation differences between groups: i) patients with a specific diagnosis vs. healthy controls, ii) subjects at high risk for a specific diagnosis vs. low risk or normal controls, and iii) patients with specific symptom(s) vs. patients without these symptom(s). The present review outlines and summarizes the studies that have employed CSD measures in investigating several neuropsychiatric conditions. The advantages and potential of CSD-based methods in clinical and research applications along with some of the limitations inherent in the CSD-based methods are discussed in the review, as well as future directions to expand the implementation of CSD to other potential clinical applications. As CSD methods have proved to be more advantageous than using scalp potential data to understand topographic and source activations, its clinical applications offer promising potential, not only for a better understanding of a range of psychiatric conditions, but also for a variety of focal neurological disorders, including epilepsy and other conditions involving brain lesions and surgical interventions.

Linking motor-related brain potentials and velocity profiles in multi-joint arm reaching movements

The study of the movement related brain potentials (MRPBs) needs accurate technical approaches to disentangle the specific patterns of bran activity during the preparation and execution of movements. During the last forty years, synchronizing the electromyographic activation (EMG) of the muscle with electrophysiological recordings (EEG) has been commonly ussed for these purposes. However, new clinical approaches in the study of motor diseases and rehabilitation suggest the demand of new paradigms that might go further into the study of the brain activity associated with the kinematics of movements. As a response to this call, we have used a 3-D hand-tracking system with the aim to record continuously the position of an ultrasonic sender attached to the hand during the performance of multi-joint self-paced movements. We synchronized time-series of position and velocity of the sender with the EEG recordings, obtaining specific patterns of brain activity as a function of the fluctuations of the kinematics during natural movement performance. Additionally, the distribution of the brain activity during the preparation and execution phases of movements was similar that reported previously using the EMG, suggesting the validity of our technique. We claim that this paradigm could be usable in patients because of its simplicity and the potential knowledge that can be extracted from clinical protocols.

Improved surface Laplacian estimates of cortical potential using realistic models of head geometry

Surface Laplacian of scalp EEG can be used to estimate the potential distribution on the cortical surface as an alternative to invasive approaches. However, the accuracy of surface Laplacian estimation depends critically on the geometric shape of the head model. This paper presents a new method for computing the surface Laplacian of scalp potential directly on realistic scalp surfaces in the form of a triangular mesh reconstructed from MRI scans. Unlike previous methods, this algorithm does not resort to any surface fitting proxy and can improve the surface Laplacian estimation of cortical potential patterns by as much as 34% on realistically shaped head models. Simulations and experimental data are presented to demonstrate the advantage of the proposed method over the conventional spherical approximation and the utility of a more accurate surface Laplacian method for estimating cortical potentials from scalp electrodes.

A comparison of tripolar concentric ring electrode and spline Laplacians on a four-layer concentric spherical model

We have simulated a four-layer concentric spherical head model. We calculated the spline and tripolar Laplacian estimates and compared them to the analytical Laplacian on the spherical surface. In the simulations we used five different dipole groups and two electrode configurations. The comparison shows that the tripolar Laplacian has higher correlation coefficient to the analytical Laplacian in the electrode configurations tested (19, standard 10/20 locations and 64 electrodes).

Very high frequency oscillations (VHFO) as a predictor of movement intentions

Gamma band (30-80 Hz) oscillations arising in neuronal ensembles are thought to be a crucial component of the neural code. Recent studies in animals suggest a similar functional role for very high frequency oscillations (VHFO) in the range 80-200 Hz. Since some intracerebral studies in humans link VHFO to epileptogenesis, it remains unclear if VHFO appear in the healthy human brain and if so which is their role. This study uses EEG recordings from twelve healthy volunteers, engaged in a visuo-motor reaction time task, to show that VHFO are not necessarily pathological but rather code information about upcoming movements. Oscillations within the range (30-200 Hz) occurring in the period between stimuli presentation and the fastest hand responses allow highly accurate (>96%) prediction of the laterality of the responding hand in single trials. Our results suggest that VHFO belong in functional terms to the gamma band that must be considerably enlarged to better understand the role of oscillatory activity in brain functioning. This study has therefore important implications for the recording and analysis of electrophysiological data in normal subjects and patients.

Amplitude modulation of gamma band oscillations at alpha frequency produced by photic driving

Gamma band response to visual stimulation in humans has been observed to have both burst and resonance properties. Amplitude modulation of gamma activity at low frequencies has been seen in rat hippocampus and modeled in a number of forms. Significant amplitude modulation (p=0.05) of 33 Hz gamma frequency activity at the frequency of an 8 1/3 Hz photic driving stimulus, which also produced strong alpha entrainment, was observed in 67% of the channels in 42 human subjects. Similar amplitude modulation was found at a range of frequencies from greater than 50 Hz to about 28 Hz. The peak of the gamma amplitude modulation curve trailed the peak of the alpha signal by 25 to 30 ms, corresponding to a phase difference of 150 degrees to 180 degrees. The phase consistency of the gamma signal, measured across comparable times of the alpha signal, was least at the minimum amplitude modulation, and largest at the maximum. Although there was no consistent overall relation between the gamma amplitude and alpha amplitude, peak gamma amplitude values were consistently higher during post-target-stimulus alpha suppression, which occurs about 300-750 ms subsequent to stimulus presentation, than they were at the time of maximum alpha activity during the immediate post-stimulus period. It is hypothesized that there is an interaction between the alpha and gamma generating systems, in which gamma triggers alpha activity and is subsequently inhibited by it, thus producing the observed amplitude modulation. The transition from dark to light of the photic driving stimulus begins a phase resetting process in the gamma system and a concomitant burst of gamma activity; this produces an activation in the alpha system, similar to that found in the P1-N1 response in evoked potential experiments, and a subsequent inhibition of gamma production.

Resistor mesh model of a spherical head: Part 2: A review of applications to cortical mapping

A resistor mesh model (RMM) has been validated with reference to the analytical model by consideration of a set of four dipoles close to the cortex. The application of the RMM to scalp potential interpolation was detailed in Part 1. Using the RMM and the same four dipoles, the different methods of cortical mapping were compared and have shown the potentiality of this RMM for obtaining current and potential cortical distributions. The lead-field matrices are well-adapted tools, but the use of a square matrix of high dimension does not permit the inverse solution to be improved in the presence of noise, as a regularisation technique is necessary with noisy data. With the RMM, the transfer matrix and the cortical imaging technique proved to be easy to implement. Further development of the RMM will include application to more realistic head models with more accurate conductivities.

Evaluation of the distortion of EEG signals caused by a hole in the skull mimicking the fontanel in the skull of human neonates

OBJECTIVE

Interpretation of Electroencephalography (EEG) signals from newborns is in some cases difficult because the fontanels and open sutures produce inhomogeneity in skull conductivity. We experimentally determined how EEG is influenced by a hole mimicking the anterior fontanel since distortion of EEG signals is important in neurological examinations during the perinatal period.

METHODS

Experiments were carried out on 10 anesthetized farm swine. The fontanel was mimicked by a hole (12 x 12 mm) in the skull. The hole was filled with 3 types of medium differing in conductivity (air, 0 S/m; sucrose-agar, 0.017 S/m; saline-agar, 1.28 S/m). Three positions of the snout were stimulated with a concentric bipolar electrode to activate cortical areas near the middle, the edge, and the outside of the hole. The somatic-evoked potential (SEP) was recorded by a 4 x 4 electrode array with a 4mm grid spacing. It was placed on the 4 quadrants of a 28 x 28 mm measurement area on a saline-soaked filter paper over the skull, which served as artificial scalp.

RESULTS

The SEP over the hole was clearly stronger when the hole was filled with sucrose- or saline-agar as compared to air, although paradoxically the leakage current was stronger for the sucrose- than saline-agar. The current leaking from the hole was strongly related to position of the active tissue. It was nearly negligible for sources 6-10 mm away from the border of the hole. The distortion was different for 3 components of the SEP elicited by each stimulus, probably indicating effects of source distance relative to the hole.

CONCLUSIONS

EEG is strongly distorted by the presence of a hole/fontanel with the distortion specifically dependent on both conductivity of the hole and source location.

SIGNIFICANCE

The distortion of the EEG is in contrast to the lack of distortion of magnetoencephalography (MEG) signals shown by previous studies. In studying brain development with EEG, the infant's head and sources should be modeled accurately in order to relate the signals to the underlying activity. MEG may be particularly advantageous over EEG for studying brain functions in infants since it is relatively insensitive to skull defects.

Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns

OBJECTIVE

To develop a single trial motor imagery (MI) classification strategy for the brain-computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description.

METHODS

The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time-frequency domains.

RESULTS

The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area.

CONCLUSIONS

The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact.

SIGNIFICANCE

The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.

Effect of Electrode Density and Measurement Noise on the Spatial Resolution of Cortical Potential Distribution

The purpose of the present study was to examine the spatial resolution of electroencephalography (EEG) by means of inverse cortical EEG solution. The main interest was to study how the number of measurement electrodes and the amount of measurement noise affects the spatial resolution. A three-layer spherical head model was used to obtain the source-field relationship of cortical potentials and scalp EEG field. Singular value decomposition was used to evaluate the spatial resolution with various measurement noise estimates. The results suggest that as the measurement noise increases the advantage of dense electrode systems is decreased. With low realistic measurement noise, a more accurate inverse cortical potential distribution can be obtained with an electrode system where the distance between two electrodes is as small as 16 mm, corresponding to as many as 256 measurement electrodes. In clinical measurement environments, it is always beneficial to have at least 64 measurement electrodes.

Overview of artifact reduction and removal in evoked potential and event-related potential recordings

Artifact in one form or another needs to be contended with in all EEG and EP studies. Various methods have been employed to avoid, eliminate, or minimize artifact. This article has described the methods that have been available for some time and newer methods and their advantages. It is the authors' hope that through the use of these methods the accuracy of all EP and ERP measurements will be improved and promote the validity and general acceptance of EP and ERP recordings. Genuine and valuable data are contained in EPs and ERPs. The challenge is to extract relatively small voltage signals often occurring within a higher voltage background of artifact. The computing power required to perform the artifact removal/reduction procedures now is available with basic laptop and desktop computers, as are the software programs that provide the artifact removal/reduction capabilities. It may be of interest for a prudent researcher to integrate the currently available artifact rejection methods before subjecting the ERP and EP data for further analysis and subsequent publication.

High-resolution EEG mapping: a radial-basis function based approach to the scalp Laplacian estimate

OBJECTIVES

The present study addressed a new scalp Laplacian mapping (LM) algorithm.

METHODS

Using a radial-basis function (RBF) as the interpolation basis function, and the smallest arc length on the surface of a spherical head model as the distance measure between two measurement sites, a new RBF based approach to LM is formulated.

RESULTS

With simulated data and empirical data, comparison between the new RBF based approach and the spherical spline function (SSF) based approach was conducted in a 4-concentric spheres head model, and the results show that the RBF based approach is better than the SSF based approach to LM.

CONCLUSIONS

The new RBF based approach to LM provides an additional efficient way for the neural electrical activities imaging.

A spline Laplacian ECG estimator in a realistic geometry volume conductor

We have developed a spline-based Laplacian estimator over an arbitrarily shaped surface of a volume conductor and tested its applicability to Laplacian electrocardiogram (ECG) mapping. In the newly developed algorithm, estimation of the parameters associated with the spline Laplacian is formulated by seeking the general inverse of a transfer matrix. Only one spline-parameter needs to be determined through regularization in order to estimate the realistic geometry surface Laplacian from the body surface potentials. It has been demonstrated that the rich knowledge on regularization in the inverse problems can be directly applied to estimate the spline Laplacian ECG (LECG), such as the discrepancy principle. Computer simulations have been conducted to validate the new approach in a spherical volume conductor and test the feasibility of mapping cardiac electrical sources in a realistic geometry heart-torso model. The present results demonstrate that the realistic geometry spline LECG can be estimated conveniently from the body surface potentials, is more robust against measurement noise and has better performance than the conventional five-point local Laplacian estimator.

Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks

A theoretical framework supporting experimental measures of dynamic properties of human EEG is proposed with emphasis on distinct alpha rhythms. Robust relationships between measured dynamics and cognitive or behavioral conditions are reviewed, and proposed physiological bases for EEG at cellular levels are considered. Classical EEG data are interpreted in the context of a conceptual framework that distinguishes between locally and globally dominated dynamic processes, as estimated with coherence or other measures of phase synchronization. Macroscopic (scalp) potentials generated by cortical current sources are described at three spatial scales, taking advantage of the columnar structure of neocortex. New EEG data demonstrate that both globally coherent and locally dominated behavior can occur within the alpha band, depending on narrow band frequency, spatial measurement scale, and brain state. Quasi-stable alpha phase structures consistent with global standing waves are observed. At the same time, alpha and theta phase locking between cortical regions during mental calculations is demonstrated, consistent with neural network formation. The brain-binding problem is considered in the context of EEG dynamic behavior that generally exhibits both of these local and global aspects. But specific experimental designs and data analysis methods may severely bias physiological interpretations in either local or global directions.

High-resolution EEG: a new realistic geometry spline Laplacian estimation technique

BACKGROUND

A new realistic geometry (RG) spline Laplacian estimation technique has been developed for high-resolution EEG imaging.

METHODS

Estimation of the parameters associated with the spline Laplacian is formulated by seeking the general inverse of a transfer matrix. The number of spline parameters, which need to be determined through regularization, is reduced to one in the present approach, thus enabling easy implementation of the RG spline Laplacian estimator.

RESULTS

Computer simulation studies have been conducted to test the feasibility of the new approach in a 3-concentric-sphere head model. The new technique has also been applied to human visual evoked potential data with a RG head model.

CONCLUSIONS

The present numerical and experimental results demonstrate the feasibility of the new approach and indicate that the RG spline Laplacian can be estimated easily from the surface potentials and the scalp geometry.

A new computational approach for cortical imaging

Estimation of current or potential distribution on the cortex is used to obtain information about neural sources from the scalp recorded electroencephalogram. If the active sources in the brain are superficial, the estimated field distribution on the cortex also yields information about the active source configuration. In these cases, these methods can be used as source localization methods. In this study, we concentrate on finite-element-based cortex potential estimation. Usually these methods require surface interpolation of the recorded voltages at the electrodes onto the entire scalp surface. We propose a new computational approach which does not require the use of surface interpolation but does it implicitly and uses only the recorded data at the electrodes. We refer to this method as the systematic approach (SA). We compare the SA with the surface interpolation approach (IA) and show that the SA is able to produce somewhat better accuracy than the IA. However, the main asset is that the sensitivity of the cortical potential maps to the regularization parameter is significantly lower than with the IA.

High-resolution EEG mappings: a spherical harmonic spectra theory and simulation results

Shown first is the equivalence between the multiple expansion (ME) of the brain electrical generator and the spherical harmonic spectra (SHS) of the potential generated by the electrical generator in an infinite volume conductor. Based on the equivalence, the SHS and the spatial filters which connect the SHS with the ME are deduced, in a concentric 3 sphere conductor and for the 5 EEG source mappings. They are cortical potential mapping (CPM), scalp Laplacian mapping (LM), pseudo-cortical potential mapping (PCPM), equivalent dipole layer mapping (EDM) and equivalent charge layer mapping (ECM). The theoretical simulation study of the spatial filters and mappings indicate that all 5 mappings provide higher resolution imaging maps of brain electrical activity than the scalp potential map. In the inverse problem, a spherical spline fit algorithm is provided to reconstruct the SHS of the scalp recording potential, and then the SHS and maps of the 5 mappings are reconstructed by utilizing the spatial filters and the SHS of the scalp potential. The results indicate that the correlativity order between a reconstructed map and the actual cortical potential map is CPM > or = EDM > PCPM > LM > ECM. An empirical VEP data study shows that any one of the 5 mappings also provides higher spatial resolution than the scalp potential map.

Event-related EEG/MEG synchronization and desynchronization: basic principles

An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.

An asymptotic estimate for the effective radius of a concentric bipolar electrode

A concentric bipolar electrode (CBE) (consisting of a central disc and an outer annulus) has been proposed as an approximate method of measuring the surface Laplacian of the body surface electrical potential distribution. The derivation of the surface Laplacian approximation, in terms of the potential difference measured with the bipolar electrode, contained an unspecified parameter which has been dubbed the 'effective radius' of the concentric bipolar electrode. This paper presents an asymptotic analysis to derive an expression for the effective radius in terms of the physical dimensions of the electrode (the radius of the central disc and inner and outer radii of the annulus). Also studied is the way in which the value of the effective radius affects the behaviour of the relative error in the surface Laplacian measurement at various dipole source locations within a conducting medium.

Electroencephalographic imaging of higher brain function

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Deblurring

In most instances, traditional EEG methodology provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. This article describes a method called Deblurring for increasing the spatial detail of the EEG and for fusing neurophysiologic and neuroanatomic data. Deblurring estimates potentials near the outer convexity of the cortex using a realistic finite element model of the structure of a subject's head determined from their magnetic resonance images. Deblurring is not a source localization technique and thus makes no assumptions about the number or type of generator sources. The validity of Deblurring has been initially tested by comparing deblurred data with potentials measured with subdural grid recordings. Results suggest that deblurred topographic maps, registered with a subject's magnetic resonance imaging and rendered in three dimensions, provide better spatial detail than has heretofore been obtained with scalp EEG recordings. Example results are presented from research studies of somatosensory stimulation, movement, language, attention and working memory. Deblurred ictal EEG data are also presented, indicating that this technique may have future clinical application as an aid to seizure localization and surgical planning.

Comparison of MEG and EEG on the basis of somatic evoked responses elicited by stimulation of the snout in the juvenile swine

OBJECTIVE

Some basic characteristics of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals were studied by comparing somatic evoked fields (SEFs) and potentials (SEPs) elicited by electrical stimulations of different areas of the snout in piglets.

METHODS

SEFs were measured with and without an intact skull, whereas SEPs were measured on the skull and cortex (Electrocorticograms - ECoG) and within the cortex of the same animal.

RESULTS

The SEFs above the skull and dura were very similar to each other in temporal waveform and spatial topography, indicating small effects of the skull. They both revealed very similar somatotopic projections of the snout. The SEPs on the skull and cortex were, in contrast, clearly different in their amplitudes as well as temporal and spatial morphologies, indicating significant effects of the skull. However, an early component of the SEP on the skull revealed a somatotopic representation of the snout, indicating that EEG can be also useful for inferring cortical projection areas. Discrepancies in their maps were due to predominance of the potentials produced by currents in the gyral cortex. The projection sites inferred from SEFs were quite accurate in comparison to those inferred from ECoGs and intracortical SEPs.

CONCLUSION

The similarities and differences clearly point out the complementary nature of MEG and EEG.

Neurophysiological indices of strategy development and skill acquisition

In order to examine neurophysiological changes associated with the development of cognitive and visuomotor strategies and skills, spectral features of the EEG were measured as participants learned to perform new tasks. In one experiment eight individuals practiced working memory tasks that required development of either spatial or verbal rehearsal and updating strategies. In a second experiment six individuals practiced a video game with a difficult visuomotor tracking component. The alpha rhythm, which is attenuated by functional cortical activation, was affected by task practice. In both experiments, a lower-frequency, centrally distributed alpha component increased between practice sessions in a task-independent fashion, reflecting an overall decrease in the extent of cortical activation after practice. A second, higher-frequency, posterior component of the alpha rhythm displayed task-specific practice effects. Practice in the verbal working memory task resulted in an increase of this signal over right posterior regions, an effect not seen after practice with the spatial working memory task or with the video game. This between-task difference presumably reflects a continued involvement of the posterior region of the right hemisphere in tasks that invoke visuospatial processes. This finding thus provides neurophysiological evidence for the formation of a task-specific neurocognitive strategy. In the second experiment a third component of the alpha rhythm, localized over somatomotor cortex, was enhanced in conjunction with acquisition of tracking skill. These alpha band results suggest that cortical regions not necessary for task performance become less active as skills develop. In both experiments the frontal midline (Fm) theta rhythm also displayed increases over the course of test sessions. This signal is associated with states of focused concentration, and its enhancement might reflect the conscious control over attention associated with maintenance of a task-appropriate mental set. Overall, the results suggest that the EEG can be used to monitor practice-related changes in the patterns of cortical activity that are associated with task processing. Additionally, these results highlight the importance of ensuring that subjects have developed stable strategies for performance before drawing inferences about the functional architecture underlying specific cognitive processes.

A rapid method for determining standard 10/10 electrode positions for high resolution EEG studies

This report describes the basic principle and examines the comparative accuracy of a novel method for locating 3-D coordinates of electrode positions on the head. The method involves calculation of the 3-D coordinates for any array of 10/10 electrode positions from 14 straight-line distances between 11 10/10 electrodes. In 11 subjects the 3-D coordinates of 64 scalp electrodes embedded in an electrode cap were identified with the novel method, and also with a standard commercial magnetic field digitizer. The outcomes from the two methods were compared with directly measured coordinates of all 64 positions (cf. De Munck, J.C., Vijn, P.C.M. and Spekreijse, H. A practical method for determining electrode positions on the head. Electroenceph. clin. Neurophysiol., 1991, 89: 85-87). Coordinates in 3 dimensions obtained using the new method were significantly closer to the directly measured values than those from the magnetic field digitizer. The new method was also quicker and requires less specialized instrumentation than the magnetic field digitization method. The novel method appears to be a valid and convenient tool for use with EEG analysis techniques that require specific information about 10/10 electrode positions.

Improved realistic Laplacian estimate of highly-sampled EEG potentials by regularization techniques

In this study we investigated the effects of lambda correction, generalized cross-validation (GCV), and Tikhonov regularization techniques on the realistic Laplacian (RL) estimate of highly-sampled (128 channels) simulated and actual EEG potential distributions. The simulated EEG potential distributions were mathematically generated over a 3-shell spherical head model (analytic potential distributions). Noise was added to the analytic potential distributions to mimic EEG noise. The magnitude of the noise was 20, 40 and 80% that of the analytic potential distributions. Performance of the regularization techniques was evaluated by computing the root mean square error (RMSE) between regularized RL estimates and analytic surface Laplacian solutions. The actual EEG data were human movement-related and short-latency somatosensory-evoked potentials. The RL of these potentials was estimated over a realistically-shaped, magnetic resonance-constructed model of the subject's scalp surface. The RL estimate of the simulated potential distributions was improved with all the regularization techniques. However, the lambda correction and Tikhonov regularization techniques provided more precise Laplacian solutions than the GCV computation (P < 0.05); they also improved better than the GCV computation the spatial detail of the movement-related and short-latency somatosensory-evoked potential distributions. For both simulated and actual EEG potential distributions the Tikhonov and lambda correction techniques provided nearly equal Laplacian solutions, but the former offered the advantage that no preliminary simulation was required to regularize the RL estimate of the actual EEG data.

Monitoring working memory load during computer-based tasks with EEG pattern recognition methods

We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants performed high-, moderate-, and low-load working memory tasks. Frontal theta EEG activity increased and alpha activity decreased with increasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a moderate load could be discriminated from high- or low-load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.

The future of electroencephalography in assessing neurocognitive functioning

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity underlying mental function. Additionally, simple, non-intrusive equipment is needed to routinely measure such functions in doctors' offices, at home and work and in other naturalistic contexts as people perform normal everyday activities. When compared with all other modalities for measuring higher brain functions, EEG is unique in that it has both these attributes. Two factors are limiting the further development and application of EEG for measuring cognitive functioning: a technical one that is easy to overcome and a sociological one that is more problematic. The technical limitation is that traditional EEG technology and practice provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging (MRI) or other modalities. Recent advances overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical information from each subject's MRI, by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp, and by computing hypotheses about the sources of signals recorded at the scalp. The sociological limitation is that clinical EEGs are mostly performed by neurologists with no particular special interest in cognitive brain function, while cognitive research using EEG is largely done by psychology professors and their graduate students with no clinical ambitions. The diminishing clinical role of traditional EEGs in localizing lesions in the brain, and the obvious and insistent medical need for inexpensive and accessible tests of cognitive brain functioning may serve to soon dissipate this sociological obstruction. This will lead to a golden age of EEG in which Hans Berger's vision of the EEG as a window on the mind will be realized. Rather than slowly fading into obsolescence, EEG will retain its role as the primary means of measuring higher brain function when the purpose is not 3D localization per se, and will serve as an invaluable complement to functional MRI in those instances when both high temporal and high spatial resolution are required.

Electrophysiological evidence of memory impairment in alcoholic patients

In a series of event-related potential (ERP) studies, we have consistently demonstrated an ERP component correlate of visual short-term memory. There have been frequent reports on the deficits of information encoding, retention, and retrieval in chronic alcoholics. In the present study, we investigated that the ERP mnemonic effects could be influenced by long-term alcohol abuse. ERP data were recorded from 48 controls and 77 alcoholics while the subjects performed a modified delayed matching to sample paradigm using a series of object pictures as stimuli. The alcoholics completed the task with more errors and longer response times than the controls. The major differences in the evoked potentials between the two groups are found at the temporo-occipital and frontal regions in the sample and nonmatching trials, and mostly prominent in the right hemisphere. The current study indicates that the ERP technique can be a useful tool to index short-term memory. The ERP mnemonic effect difference between the two groups may be a reflection of a working memory deficit caused by long-term alcohol abuse. Our data also suggest right hemisphere dysfunction in alcoholics, with deficits in information encoding.

EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales

Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, 'reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.

A high resolution EEG method based on the correction of the surface Laplacian estimate for the subject's variable scalp thickness

To improve the spatial resolution of human event-related potentials, we developed a new high resolution EEG method based on the improved estimate of the realistic surface Laplacian (SL). The novelty of this method consisted in the computation of the local scalp resistance that was assumed to be inversely proportional to the local scalp thickness measured from magnetic resonance images of the subject's head. The local scalp thickness was then multiplied by the SL estimate of the potential over a realistic magnetic resonance-constructed model of the subject's scalp surface. The new method was applied on human movement-related and somatosensory-evoked potentials, the SL estimate at a constant scalp thickness being used as a reference. The locally-predicted scalp thickness was significantly (P < 0.05) higher in the temporal areas (9.5 +/- 2.6 mm) than in the parieto-occipital (6.6 +/- 1.3 mm) and frontal (4.8 +/- 1.1 mm) areas. Compared to the SL estimate at constant scalp thickness, the improved SL estimate enhanced the spatial detail of both movement-related and somatosensory-evoked potentials.

Intra-hemispheric alpha coherence decreases with increasing cognitive impairment in HIV patients

Inter-hemispheric and intra-hemispheric canonical coherences in the alpha range between EEG signals collected from frontal and posterior groups of electrodes were estimated for 38 HIV positive subjects and 23 uninfected controls. Neuropsychological testing was used to categorize the degree of cognitive impairment evident in each of the subjects. A linear regression analysis provided evidence that intra-hemispheric coherence decreased with increasing cognitive impairment in impaired HIV+ subjects, as measured by a Global Impairment Score (GIS). There was no evidence that cognitively unimpaired HIV+ subjects differed in coherence when compared to uninfected control subjects. Severely impaired HIV+ subjects showed significantly decreased coherence compared to uninfected controls. These data contradict previous work demonstrating increased intra-hemispheric and inter-hemispheric alpha coherence in impaired HIV subjects. In addition, they provide evidence that intra-hemispheric (and possibly inter-hemispheric) disconnection is associated with cognitive impairment in HIV.

Implications of electrolyte dispersion for high resolution EEG methods

The effective recording area of an EEG electrode is its electrical contact area with the scalp. With techniques that employ wet electrolyte, this area is primarily determined by the extent of electrolyte dispersion rather than by the size of the electrode. The effective recording areas of 10 widely distributed EEG electrodes embedded in an elasticized stretch hat were measured on 7 subjects using a digital multimeter. On average, conventionally prepared electrodes were associated with an electrolyte (standard gel) spread of approximately 1 cm in each of four directions (above, below, right and left of the electrode's center). This implies that EEG electrodes prepared with wet electrolyte should not be spaced less than 2 cm apart unless special precautions are taken to prevent the spread of electrolyte, and that in most circumstances there is little advantage to methods for designating the 3-D coordinates of an electrode that have a measurement error of less than 1 cm.

High resolution EEG: a new model-dependent spatial deblurring method using a realistically-shaped MR-constructed subject's head model

This paper presents a new model-dependent method for the spatial deblurring of scalp-recorded EEG potentials based on boundary-element and cortical imaging techniques. This model-dependent spatial deblurring (MDSD) method used MR images for the reconstruction of the subject's head model, and a layer of 364 radially-oriented equivalent current dipoles as a source model. The validation of the MDSD method was performed on simulated potential distributions generated from equivalent dipoles oriented radially, obliquely, and tangentially to the head surface. Furthermore, this method was used to localize neocortical sources of human movement-related and somatosensory-evoked potentials. It was shown that the new MDSD method improved markedly the spatial resolution of the simulated surface potentials and scalp-recorded event-related potentials. The spatial information content of the scalp-recorded EEG potentials increased progressively by increasing the spatial sampling from 28 to 128 channels. These results indicate that the new method could be satisfactorily used for high resolution EEG studies.

Optimal reference electrode selection for electric source imaging

One goal of recording voltages on the scalp is to form images of electrical sources across the cerebral cortex (electric source imaging). In this study, an objective criterion is introduced for selecting the optimal location for the reference electrode to attain the maximum spatial resolution of the source image, for example as provided here by the truncated singular value decomposition pseudo-inverse solution. The head model features a realistic cortex within a 3-shell conductive sphere, and pyramidal cell activity is represented by 9104 normal current elements distributed across the cortical area. On the scalp, 234 electrodes provide the measurements with respect to a chosen reference electrode. The effects of the reference electrode when located at the mastoid, occipital pole, vertex or center of the head are analyzed by a singular value decomposition of the lead field matrices. Sensitivity to noise, and hence the spatial resolution, is found to depend on characteristics of the lead field matrix that are determined by the choice of the image source surface, electrode array and location of the reference electrode. Using a reference close to a source surface increases the sensitivity of the measurement system in identifying the nearby activity of low spatial frequency content. However, this feature is compromised by a reduction in spatial resolution for distant cortical areas due to noise in the measurements. A new performance measure, the image sensitivity map, is introduced to identify the cortical regions that provide peak image sensitivity. This measure may be exploited in designing the geometry of an electrode array and selecting the location of the reference electrode to follow the activity on a specific area of the cortical surface.

Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model

This paper presents a realistic Laplacian (RL) estimator based on a tensorial formulation of the surface Laplacian (SL) that uses the 2-D thin plate spline function to obtain a mathematical description of a realistic scalp surface. Because of this tensorial formulation, the RL does not need an orthogonal reference frame placed on the realistic scalp surface. In simulation experiments the RL was estimated with an increasing number of "electrodes" (up to 256) on a mathematical scalp model, the analytic Laplacian being used as a reference. Second and third order spherical spline Laplacian estimates were examined for comparison. Noise of increasing magnitude and spatial frequency was added to the simulated potential distributions. Movement-related potentials and somatosensory evoked potentials sampled with 128 electrodes were used to estimate the RL on a realistically shaped, MR-constructed model of the subject's scalp surface. The RL was also estimated on a mathematical spherical scalp model computed from the real scalp surface. Simulation experiments showed that the performances of the RL estimator were similar to those of the second and third order spherical spline Laplacians. Furthermore, the information content of scalp-recorded potentials was clearly better when the RL estimator computed the SL of the potential on an MR-constructed scalp surface model.

High resolution evoked potential imaging of the cortical dynamics of human working memory

High resolution evoked potentials (EPs), sampled from 115 channels and spatially sharpened with the finite element deblurring method, were recorded from 8 subjects during working memory (WM) and control tasks. The tasks required matching each stimulus with a preceding stimulus on either verbal or spatial attributes. All stimuli elicited a central P200 potential that was larger in the spatial tasks than in the verbal tasks, and larger in the WM tasks than in the control tasks. Frequent, non-matching stimuli elicited a frontal, positive peak at 305 msec that was larger in the spatial WM task relative to the other tasks. Irrespective of whether subjects attended to verbal or spatial stimulus attributes, non-matching stimuli in the WM tasks also elicited an enhanced P450 potential over the left frontal cortex, followed by a sustained potential over the superior parietal cortex. A posterior P390 potential elicited by infrequent, matching stimuli was smaller in amplitude for both spatial and verbal WM tasks compared to control tasks, as was a central prestimulus CNV. These results indicate that WM is a function of a distributed system with both task-specific and task-independent components. Lesion studies and course temporal resolution functional imaging methods, such as PET and fMRI, tend to paint a fairly static picture of the cortical regions which participate in the performance of WM tasks. In contrast, the fine-grain time resolution provided by imaging brain function with EP methods provides a dynamic picture of subsecond changes in the spatial distribution of WM effects over the course of individual trials, as well as evidence for differences in the activity elicited by matching and non-matching stimuli within sequences of trials. This information about the temporal dynamics of WM provides a critical complement to the fine-grain spatial resolution provided by other imaging modalities.

Mapping cognitive brain function with modern high-resolution electroencephalography

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. While electroencephalography (EEG) provides temporal resolution in the millisecond range, which would seem to make it an ideal complement to other imaging modalities, traditional EEG technology and practice provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions that are visualized by magnetic resonance imaging (MRI) or positron emission tomography (PET). Recent advances overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical information from each subject's MRI, and by correcting the distortion that is caused by volume conduction of EEG signals through the skull and scalp. Along with its ability to record how brains think when performing everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Performances of surface Laplacian estimators: a study of simulated and real scalp potential distributions

This paper presents a study of the performance of various local and spherical spline methods currently in use for the surface Laplacian (SL) estimate of scalp potential distributions. The SL was estimated from simulated instantaneous event-related scalp potentials generated over a three-shell spherical head model. Laplacian estimators used planar and spherical scalp models. Noise of increasing magnitude and spatial frequency was added to the potential distributions in order to simulate noise presumed to contaminate scalp-recorded event-related potentials. A comparison of noise effects on various Laplacian estimates was made for increasing number of "electrode" positions in variants of the 10-20 system. Furthermore, to evaluate the error due to the use of unrealistic scalp models, the matching between SL estimates of human scalp-recorded movement-related potentials computed on spherical and realistically-shaped MRI-constructed models of the scalp was examined. With all methods the error of the SL estimate increased proportionally with the magnitude and spatial frequency of noise. Increased number of "electrodes" up to 256 significantly reduced the error (p < 0.05). In general, the best SL estimates were computed by second and third order splines including lambda correction, the performances of the second order spline being better with more than 64 "electrodes". Compared with spline Laplacians, the best local methods provided nearly equal estimates with low spatial sampling (19 and 28 "electrodes"), as well as high spatial frequency noise. The error of the SL estimate due to unrealistic scalp model was significant, and it augmented with increased spatial sampling from 64 to 128 electrodes.

Towards measurement of brain function in operational environments

In operational environments that demand sustained vigilance or that involve multiple tasks competing for limited attentional resources, continuous monitoring of the mental state of the operator could decrease the potential for serious errors and provide valuable information concerning the ergonomics of the tasks being performed. There is widespread discussion and appreciation of the basic feasibility of utilizing neurophysiological measurements to derive accurate, reliable, rapid and unobtrusive assessments of mental state. However, progress in transitioning this idea into practical applications has been impeded by the fact that at present no convenient, inexpensive and effective means exists to derive a meaningful index of brain activity outside of laboratory settings. In this paper, we review some recent advances in recording technology and signal processing methods that will help overcome this limitation. For example, rapid progress is being made in the engineering of recording systems that are small, rugged, portable and easy-to-use, and thus suitable for deployment in operational environments. Progress is also being made in the development of signal processing algorithms for detecting and correcting recording artifacts and for increasing the amount of useful information that can be derived from brain signals. Finally, results from basic research studies suggest that accurate and reliable inferences about the mental load and alertness of an individual can be derived from neurophysiological measures in a practical fashion. These research and engineering successes suggest that it is reasonable to expect that in the near term a basic enabling technology will be deployed that will permit routine measurement of brain function in operational environments.