EEG Dipole Source Localization Research Articles

Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming

In this paper, we propose new neural activity indices for the solution of the inverse problem of localizing sources of cortical activity from electroencephalography (EEG) measurements. Such indices are based on reduced-rank beamformers, specifically the generalized sidelobe canceler (GSC), and with the purpose of suppressing the contribution of interfering sources and noise. Here, the GSC is modified with an adaptive blocking matrix (ABM) to optimally estimate and later suppress unwanted brain sources. With respect to the rank-reduction, this is achieved through the cross-spectral metrics (CSM) as they give a sense of the affinity of the beamformers’ eigenstructure to the orthogonal subspace of noise an interference. Based on that, two different neural indices are proposed for the assessment of brain activation. Our realistic simulations show that a more consistent source localization is achieved through the proposed indices in comparison to the use of the traditional full-rank approach, specifically for brain sources embedded in high background activity that originates at the brain cortex and thalamus. We also prove the applicability of our methods on the localization of sources on the visual cortex produced by steady-state visual-evoked potentials.

Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study.

Auditory alarms are used to direct people’s attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.

EEG dipole source localization with information criteria for multiple particle filters

Electroencephalography (EEG) is a non-invasive brain imaging technique that describes neural electrical activation with good temporal resolution. Source localization is required for clinical and functional interpretations of EEG signals, and most commonly is achieved via the dipole model; however, the number of dipoles in the brain should be determined for a reasonably accurate interpretation. In this paper, we propose a dipole source localization (DSL) method that adaptively estimates the dipole number by using a novel information criterion. Since the particle filtering process is nonparametric, it is not clear whether conventional information criteria such as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can be applied. In the proposed method, multiple particle filters run in parallel, each of which respectively estimates the dipole locations and moments, with the assumption that the dipole number is known and fixed; at every time step, the most predictive particle filter is selected by using an information criterion tailored for particle filters. We tested the proposed information criterion first through experiments on artificial datasets; these experiments supported the hypothesis that the proposed information criterion would outperform both AIC and BIC. We then analyzed real human EEG datasets collected during an auditory short-term memory task using the proposed method. We found that the alpha-band dipoles were localized to the right and left auditory areas during the auditory short-term memory task, which is consistent with previous physiological findings. These analyses suggest the proposed information criterion can work well in both model and real-world situations.

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: a pilot study.

Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full-term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool.

Subspace electrode selection methodology for the reduction of the effect of uncertain conductivity values in the EEG dipole localization: a simulation study using a patient-specific head model

The simulation of the electroencephalogram (EEG) using a realistic head model needs the correct conductivity values of several tissues. However, these values are not precisely known and have an influence on the accuracy of the EEG source analysis problem. This paper presents a novel numerical methodology for the increase of accuracy of the EEG dipole source localization problem. The presented subspace electrode selection (SES) methodology is able to limit the effect of uncertain conductivity values on the solution of the EEG inverse problem, yielding improved source localization accuracy. We redefine the traditional cost function associated with the EEG inverse problem and introduce a selection procedure of EEG potentials. In each iteration of the presented EEG cost function minimization procedure, potentials are selected that are affected as little as possible by the uncertain conductivity value. Using simulation data, the proposed SES methodology is able to enhance the neural source localization accuracy dependent on the dipole location, the assumed versus actual conductivity and the possible noise in measurements.

EEG and MEG in mesial temporal lobe epilepsy: Where do the spikes really come from?

Objective There is persistent debate as to whether or not EEG and MEG recordings in patients with mesial temporal lobe epilepsy (MTLE) can detect mesial temporal interictal epileptiform discharges (spikes), and this issue is particularly relevant for source localization studies. With the aim of providing direct evidence pertinent to this debate we present detailed examples of the intracranial sources of spikes recorded with EEG and MEG in MTLE. Methods Spikes recorded in five different patients with MTLE during intracranial EEG ( n = 2), intraoperative electrocorticography (ECOG; n = 1), combined scalp-intracranial EEG ( n = 2) and combined EEG-MEG ( n = 1) were analyzed and the intracranial sources of the spike foci were matched with their corresponding extracranial EEG and/or MEG fields. EEG and MEG dipole source localization was performed on six independent spike foci identified in one representative patient with bilateral MTLE. Results Spikes with an electrical field maximal at F7/8, F9/10 ⩾ T3/4 were generated in the anterolateral temporal neocortex. The absence of coincident spiking at mesial locations indicated that these were not propagated from or to the hippocampus. Spikes with an electrical field maximal at T3/4 ⩾ T9/10 were generated in the lateral temporal neocortex and likewise did not involve the hippocampus. Individual spikes generated in the mesiobasal temporal neocortex, including the fusiform gyrus, were difficult to detect with EEG (low amplitude diphasic waves most apparent after spike averaging at T3/4, T9/10 ⩾ T5/6, P9/10) and only slightly more identifiable with MEG. Spikes generated within and confined to the mesial temporal structures, as confirmed by intracranial recordings, could not be detected with EEG or MEG. Notably, such spikes could not be detected even at intracranial recording sites on the lateral surface of the temporal lobe. Conclusions We present detailed evidence in a small case series showing that typical anterior temporal spikes recorded with EEG and MEG in MTLE arose from the anterolateral temporal neocortex and were neither propagated from nor to the hippocampus. Mid temporal EEG spikes were localized to the lateral temporal neocortex. Intracranially detected mesial temporal spikes were not detected with EEG or MEG. Significance The spikes recorded with EEG and MEG in MTLE are localized to neocortical foci, and not to the mesial temporal structures. Current noninvasive EEG and MEG source localization studies cannot accurately identify true mesial temporal spikes.

EEG dipole source localization of interictal spikes in non-lesional TLE with and without hippocampal sclerosis

A subgroup of patients with non-lesional temporal lobe epilepsy (NLTLE) have no evidence of hippocampal sclerosis (HS) on MRI or on histopathology. It is controversial whether this represents a different clinicopathological syndrome from NLTLE with HS, or whether both subgroups represent different ends of the spectrum of mesial TLE. Here the EEG source localization dipoles were compared between NLTLE patients with HS (HS+) and without HS (HS-), and the relationship with post-surgical outcome was investigated. EEG dipole source localization of interictal epileptiform spikes recorded during prolonged video-EEG monitoring was performed from 22 consecutive HS+ and 12 HS- NLTLE patients. EEG was acquired using 29 scalp electrodes, including an inferior temporal row. Up to 13 spikes per patient were averaged and sources localized using a boundary element model based on the patients volumetric MRI. 21/34 patients (62%) had dipoles for the interictal spikes localized to the epileptogenic temporal lobe. The site of the intratemporal localization did not differ significantly between the HS+ and HS- patients, with the dipoles localizing to the mesial temporal region in 27% of HS+ and 25% of HS- patients. There was no significant relationship between the localization and orientation of the dipoles and the surgical outcome. The dipoles for interictal spikes do not differ between HS+ and HS- patients, suggesting that these subgroups of NLTLE patients do not have distinct cerebral generators.

A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization

Many implementations of electroencephalogram (EEG) dipole source localization neglect the anisotropical conductivities inherent to brain tissues, such as the skull and white matter anisotropy. An examination of dipole localization errors is made in EEG source analysis, due to not incorporating the anisotropic properties of the conductivity of the skull and white matter. First, simulations were performed in a 5 shell spherical head model using the analytical formula. Test dipoles were placed in three orthogonal planes in the spherical head model. Neglecting the skull anisotropy results in a dipole localization error of, on average, 13.73 mm with a maximum of 24.51 mm. For white matter anisotropy these values are 11.21 mm and 26.3 mm, respectively. Next, a finite difference method (FDM), presented by Saleheen and Kwong (1997 IEEE Trans. Biomed. Eng. 44 800–9), is used to incorporate the anisotropy of the skull and white matter. The FDM method has been validated for EEG dipole source localization in head models with all compartments isotropic as well as in a head model with white matter anisotropy. In a head model with skull anisotropy the numerical method could only be validated if the 3D lattice was chosen very fine (grid size ⩽2 mm).

An alternative subspace approach to EEG dipole source localization

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

Improving lesion conductivity estimate by means of EEG source localization sensitivity to model parameter.

EEG-based source localization techniques use scalp-potential data to estimate the location of underlying neural activity. EEG source location reconstruction requires the assumption of a source model and the assumption of a conductive head model. Brain lesions can present conductivity values that are dramatically different from those of surrounding normal tissues and have to be included in head models for accurate neural source reconstruction. It is therefore necessary to analyze subjects' anatomic images (using MRI or computed tomography) to identify lesion type and to assign the appropriate conductivity value. Source localization accuracy may be influenced by uncertainties in tissue conductivity assignment during head model construction. The authors present a sensitivity study quantifying the effect of uncertainty in brain lesion conductivity assignment on EEG dipole source localization. They adopted an eccentric-spheres head model in which an eccentric bubble approximated the effects of actual brain lesions. After simulating EEG signal measurement in 64 different pathologic situations, an inverse dipole fitting procedure was carried out, assuming an incorrect lesion conductivity assignment ranging from a half to twice the real value. Incorrect lesion conductivity assignment led to markedly wrong source reconstruction for highly conductive lesions like liquid-filled ones (localization errors as much as 1.7 cm). Conversely, low sensitivity to uncertainties in conductivity assignment was found for lesions with low conductivity like calcified tumors. The authors propose a method based on residual error analysis to improve the lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error and guarantees source localization errors less than 5 mm.

The influence of electrode location errors on EEG dipole source localization with a realistic head model

Objectives: Inaccurate information about the electrode locations on the scalp will introduce errors in electroencephalogram dipole source localization results. The present study uses computer simulations to evaluate such errors in a realistic head model and in the context of noise. Methods: A realistic head model was constructed from magnetic resonance imaging scans and 29 electrodes placed on the head according to the 10–20 International System. Twenty sets of electrode displacements, with a mean value of 5 mm, were generated and 200 single dipoles evenly located in the brain were used as test sources. The boundary element method was employed for the froward calculation and dipole fitting was carried out at different noise levels. Results: For a noise-free signal, the source localization error due to electrode misplacement is about 5 mm, whereas it is about 2 mm for normal noisy signals. Conclusions: For realistic head models, dipole estimation error due to electrode misplacement is negligible compared with errors caused by noise.

EEG dipole source localization using artificial neural networks

Localization of focal electrical activity in the brain using dipole source analysis of the electroencephalogram (EEG), is usually performed by iteratively determining the location and orientation of the dipole source, until optimal correspondence is reached between the dipole source and the measured potential distribution on the head. In this paper, we investigate the use of feed-forward layered artificial neural networks (ANNs) to replace the iterative localization procedure, in order to decrease the calculation time. The localization accuracy of the ANN approach is studied within spherical and realistic head models. Additionally, we investigate the robustness of both the iterative and the ANN approach by observing the influence on the localization error of both noise in the scalp potentials and scalp electrode mislocalizations. Finally, after choosing the ANN structure and size that provides a good trade-off between low localization errors and short computation times, we compare the calculation times involved with both the iterative and ANN methods. An average localization error of about 3.5 mm is obtained for both spherical and realistic head models. Moreover, the ANN localization approach appears to be robust to noise and electrode mislocations. In comparison with the iterative localization, the ANN provides a major speed-up of dipole source localization. We conclude that an artificial neural network is a very suitable alternative for iterative dipole source localization in applications where large numbers of dipole localizations have to be performed, provided that an increase of the localization errors by a few millimetres is acceptable.

Accuracy of EEG dipole source localization using implanted sources in the human brain

Objectives: The location of electrical sources in the brain can be estimated by calculating inverse solutions in which the location, amplitude and orientation of the electrical sources are fitted to the scalp EEG. To assess localization accuracy of the moving dipole inverse solution algorithm (ISA), we studied two patients who had depth electrodes implanted for presurgical planning of epilepsy surgery. Methods: Artificial dipoles were created by connecting a single sine wave pulse generator to different pairs of electrodes in multiple orientations and depths. Surface EEG recordings of the resulting pulses were evaluated with the ISA using a 4-shell spherical head model and plotted on the subjects' MRI. Dipole localization errors were evaluated with respect to the number of averaged pulses, different electrode montages and different dipole locations and orientations. Results: Dipoles located at 40–57 mm from the scalp surface had localization errors that were greater than those located at 62–85 mm. Localization accuracy improved with increasing numbers of pulses and recording electrodes. Results with a standard 10–20 array of 21 electrodes showed an average localization error of 17 mm, whereas 41 electrodes improved this to 13 mm. Mean angular errors were 31 and 30 degrees, respectively. Conclusions: The ISA was able to differentiate between tangential and radial dipoles. We conclude that our implementation of the ISA is a useful and sound method for localizing electrical activity in the brain.

Clinical applications of human brain mapping. Relationship between EEG dipole source localization and postoperative outcome in patients with medically refractory epilepsy.

Clinical applications of human brain mapping. Relationship between EEG dipole source localization and postoperative outcome in patients with medically refractory epilepsy.

An efficient algorithm for computing multishell spherical volume conductor models in EEG dipole source localization.

Computationally localizing electrical current sources of the electroencephalographic signal requires a volume conductor model which relates theoretical scalp potentials to the dipolar source located within the modeled brain. The commonly used multishell spherical model provides this source-potential relationship using a sum of infinite series whose computation is difficult. This paper provides a closed-form approximation to this sum based on an optimal fitting to the weights of the Legendre polynomials. The second-order (third-order) approximation algorithm, implemented by a provided C-routine, requires only 100 (140) floating point operations to compute a single scalp potential in response to an arbitrary current dipole located within a four-shell spherical volume conductor model. This cost of computation represents only 6.3% (8.9%) of that required by the direct method. The relative mean square error, measured by using 20,000 random dipoles distributed within the modeled brain, in only 0.29% (0.066%).