EEG Source Localization Research Articles

Range-based ICA using a nonsmooth quasi-newton optimizer for electroencephalographic source localization in focal epilepsy.

Independent component analysis (ICA) aims at separating a multivariate signal into independent nongaussian signals by optimizing a contrast function with no knowledge on the mixing mechanism. Despite the availability of a constellation of contrast functions, a Hartley-entropy-based ICA contrast endowed with the discriminacy property makes it an appealing choice as it guarantees the absence of mixing local optima. Fueled by an outstanding source separation performance of this contrast function in practical instances, a succession of optimization techniques has recently been adopted to solve the ICA problem. Nevertheless, the nondifferentiability of the considered contrast restricts the choice of the optimizer to the class of derivative-free methods. On the contrary, this letter concerns a Riemannian quasi-Newton scheme involving an explicit expression for the gradient to optimize the contrast function that is differentiable almost everywhere. Furthermore, the inexact line search insisting on the weak Wolfe condition and a terminating criterion befitting the partly smooth function optimization have been generalized to manifold settings, leaving the previous results intact. The investigations with diversified images and the electroencephalographic (EEG) data acquired from 45 focal epileptic subjects demonstrate the efficacy of our approach in terms of computational savings and reliable EEG source localization, respectively. Additional experimental results are available in the online supplement.

Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization.

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmentation approach (HSA)-Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)-FMRIB's automated segmentation tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20% bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3% noise and synthetic EEG (generated for a prescribed source). The source localization accuracy was determined in terms of localization error and relative error of potential. The experimental results demonstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and suggest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.

Comparison of EEG source localization using meromorphic approximation to fuzzy C-mean

Electroencephalography (EEG) is a neuroimaging technique for localizing active sources within the brain, from knowledge of electromagneticmeasurements outside the head. Recognition of point sources from boundary measurements is an ill-posed inverse problem. InEEG, measurements areonly accessible at electrode positions, the number of sources is not known a prior. This paper proposes a comparison between two approaches for EEGsource localization. First method based on Meromorphic approximation techniques in the complex plane and second one belongs to EEG’s methodwhich is processed using Fuzzy C-Means (FCM). Comparison results on simulated data are used to verify the superior of the Meromorphicapproximation with regarding to Fuzzy c-means, due to it provides the way for solving inverse problem of EEG source localization in 3D from boundarymeasurement based on Harmon function in the innermost layer .

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography.

Functional near-infrared spectroscopy (fNIRS) is an optical method for noninvasively determining brain activation by estimating changes in the absorption of near-infrared light. Diffuse optical tomography (DOT) extends fNIRS by applying overlapping “high density” measurements, and thus providing a three-dimensional imaging with an improved spatial resolution. Reconstructing brain activation images with DOT requires solving an underdetermined inverse problem with far more unknowns in the volume than in the surface measurements. All methods of solving this type of inverse problem rely on regularization and the choice of corresponding regularization or convergence criteria. While several regularization methods are available, it is unclear how well suited they are for cerebral functional DOT in a semi-infinite geometry. Furthermore, the regularization parameter is often chosen without an independent evaluation, and it may be tempting to choose the solution that matches a hypothesis and rejects the other. In this simulation study, we start out by demonstrating how the quality of cerebral DOT reconstructions is altered with the choice of the regularization parameter for different methods. To independently select the regularization parameter, we propose a cross-validation procedure which achieves a reconstruction quality close to the optimum. Additionally, we compare the outcome of seven different image reconstruction methods for cerebral functional DOT. The methods selected include reconstruction procedures that are already widely used for cerebral DOT [minimum l2-norm estimate (l2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum l1- and a smooth minimum l0-norm estimate (l1MNE, l0MNE, respectively)] and a depth- and noise-weighted minimum norm (wMNE). Furthermore, we expand the range of algorithms for DOT by adapting two EEG-source localization algorithms [sparse basis field expansions and linearly constrained minimum variance (LCMV) beamforming]. Independent of the applied noise level, we find that the LCMV beamformer is best for single spot activations with perfect location and focality of the results, whereas the minimum l1-norm estimate succeeds with multiple targets.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout.

This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R = 0.46 ± 0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R = 0.43 ± 0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Source-space ICA for EEG source separation, localization, and time-course reconstruction

We propose source-space independent component analysis (ICA) for separation, tomography, and time-course reconstruction of EEG and MEG source signals. Source-space ICA is based on the application of singular value decomposition and ICA on the neuroelectrical signals from all brain voxels obtained post minimum-variance beamforming of sensor-space EEG or MEG. We describe the theoretical background and equations, then evaluate the performance of this technique in several different situations, including weak sources, bilateral correlated sources, multiple sources, and cluster sources. In this approach, tomographic maps of sources are obtained by back-projection of the ICA mixing coefficients into the source-space (3-D brain template). The advantages of source-space ICA over the popular alternative approaches of sensor-space ICA together with dipole fitting and power mapping via minimum-variance beamforming are demonstrated. Simulated EEG data were produced by forward head modeling to project the simulated sources onto scalp sensors, then superimposed on real EEG background. To illustrate the application of source-space ICA to real EEG source reconstruction, we show the localization and time-course reconstruction of visual evoked potentials. Source-space ICA is superior to the minimum-variance beamforming in the reconstruction of multiple weak and strong sources, as ICA allows weak sources to be identified and reconstructed in the presence of stronger sources. Source-space ICA is also superior to sensor-space ICA on accuracy of localization of sources, as source-space ICA applies ICA to the time-courses of voxels reconstructed from minimum-variance beamforming on a 3D scanning grid and these time-courses are optimally unmixed via the beamformer. Each component identified by source-space ICA has its own tomographic map which shows the extent to which each voxel has contributed to that component.

Effect of EEG electrode number on epileptic source localization in pediatric patients

ObjectiveTo investigate the relationship between EEG source localization and the number of scalp EEG recording channels. Methods128 EEG channel recordings of 5 pediatric patients with medically intractable partial epilepsy were used to perform source localization of interictal spikes. The results were compared with surgical resection and intracranial recordings. Various electrode configurations were tested and a series of computer simulations based on a realistic head boundary element model were also performed in order to further validate the clinical findings. ResultsThe improvement seen in source localization substantially decreases as the number of electrodes increases. This finding was evaluated using the surgical resection, intracranial recordings and computer simulation. It was also shown in the simulation that increasing the electrode numbers could remedy the localization error of deep sources. A plateauing effect was seen in deep and superficial sources with further increasing the electrode number. ConclusionThe source localization is improved when electrode numbers increase, but the absolute improvement in accuracy decreases with increasing electrode number. SignificanceIncreasing the electrode number helps decrease localization error and thus can more ably assist the physician to better plan for surgical procedures.

LP31: EEG and MEG source localization of the epileptogenic foci in tuberous sclerosis complex: a pediatric case report

LP31: EEG and MEG source localization of the epileptogenic foci in tuberous sclerosis complex: a pediatric case report

Improving electroencephalographic source localization of epileptogenic zones with time-frequency analysis.

The combination of recently developed methods for electroencephalographic (EEG) space-time-frequency analysis can provide noninvasive functional neuroimages necessary for obtaining an accurate localization of the epileptogenic zone. The aim of this study was to determine if time-frequency (TF) analysis, followed by EEG source localization, would improve the detection and identification of epileptogenic and related activity. Seventeen patients with refractory frontal lobe epilepsy (FLE) were studied using video EEG recording. TF analysis identified the first epileptogenic EEG changes. Using the Bayesian model averaging (BMA) approach, we compared brain electromagnetic tomographic (BET) images, constructed from the TF domain, with BET images constructed from the time domain only. We determined if the localization identified by BET images was concordant with the localization from medical history and video EEG recording. TF analysis provided a clear display of subtle EEG features, including EEG lateralization, and more concordant and delimited epileptogenic zones, compared with time-domain source analysis. In conclusion, EEG TF analysis improves source localization. After a thorough validation, this methodology could become a useful noninvasive tool for localizing the epileptogenic zone in clinical practice.

Electro- and magneto-encephalographic spike source localization of small focal cortical dysplasia in the dorsal peri-rolandic region

ObjectiveSmall focal cortical dysplasia (FCD) may be ambiguous or overlooked on magnetic resonance (MR) imaging. Source localization of EEG and magnetoencephalography (MEG) spikes was evaluated to confirm the diagnosis of small FCD. MethodsThis study included 6 epilepsy patients with a single small lesion on MR imaging suggesting FCD within a single gyrus among 181 consecutive epilepsy patients admitted to our epilepsy monitoring unit over 27months. Stereotypical interictal spikes were detected on simultaneous EEG and MEG recordings and the onset-related source of averaged spikes was estimated. ResultsAll 6patients had unique clinical characteristics as follows: leg sensori-motor seizures in 5patients and eye version in 1patient; a small MR imaging lesion suggesting FCD in the dorsal peri-rolandic region, which had been overlooked until our evaluation; and both EEG and MEG dipoles were estimated adjacent to the MR imaging lesion. ConclusionsSource localization of EEG and MEG spikes can confirm the diagnosis of FCD based on a single small MR imaging lesion, which was overlooked by previous examination of MR images. SignificanceExamination of MR images should be based on spike source localization as well as seizure semiology to identify subtle MR imaging abnormalities.

42. Detection of emotional states based on scalp and source maps of EEG

Recent methods of emotional states detection are mostly based on low-density EEG recording and analysis of the scalp activity. This disadvantage should result in the low classification accuracy of emotions. We extend the research in this area to high-density EEG recordings (118 electrodes) and source localization methods. Our experiment consisted of 100 affective pictures (mostly IAPS dataset) standing for three different emotional categories (positive, neutral and negative). They were presented to 30 participants and the EEG signal was recorded. The spectral bands were calculated for scalp projection and further ICA decomposition and dipole fitting was applied for source localization. We analyzed classification errors for specific emotional categories. The goal of our study was to identify differences between low-density and high-density density EEG recordings. We were interested in the difference between scalp maps and the localized sources of the EEG signal. These differences should clarify possible sources of classification errors. We also analyzed whether differences in the spectral activity of specific emotions on the scalp correlates with the source signals. The advantages and disadvantages of specific methods are discussed.

Ictal EEG Source Imaging in Frontal Lobe Epilepsy Leads to Improved Lateralization Compared With Visual Analysis

To investigate the utility of EEG source imaging to lateralize ictal patterns in frontal lobe epilepsy, which were nonlateralized by standard EEG analysis. Prospective analysis of 17 seizures in 8 patients with unilateral frontal lobe epilepsy MRI lesions and nonlateralizing ictal scalp EEG. We applied four EEG source imaging techniques (phase maps, symmetric dipoles, low-resolution brain electromagnetic tomography analysis, and classical low-resolution brain electromagnetic tomography analysis recursively applied) to the averaged seizure pattern. We tested (1) the ability of these techniques to lateralize seizure patterns, (2) the agreement of the lateralization result with MRI lesion side and subdural EEG recordings, individually for each method and for concordance of all. We found lateralizing results in 5 of 17 seizures when analyzing phase maps, 8 of 17 when analyzing dipoles, and 5 of 17 in both classical low-resolution brain electromagnetic tomography analysis recursively applied and low-resolution brain electromagnetic tomography analysis. No discordance with the MRI lesion side was seen when analyzing phase maps, whereas dipole analysis was discordant to the MRI lesion in two seizures, classical low-resolution brain electromagnetic tomography analysis recursively applied in one, and low-resolution brain electromagnetic tomography analysis in two. Agreement between all imaging methods was found in three seizures (three patients), all in line with the side of the MRI lesion. Advanced EEG review methods and source localization provide useful lateralizing information in difficult frontal lobe epilepsy seizure patterns.

Multimodal spatial calibration for accurately registering EEG sensor positions.

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain.

Resting EEG Source Localisation in Young and Older Adults

Resting EEG Source Localisation in Young and Older Adults

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.

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.

EEG source imaging of anterior temporal lobe spikes: Validity and reliability

ObjectiveTo assess the reliability and validity of EEG source localization of anterior temporal lobe spikes through direct comparison with simultaneously recorded intracranial spike fields. MethodsWe recently showed that classical anterior temporal spikes recorded in mesial temporal lobe epilepsy (MTLE) are non-propagated potentials generated in the anterolateral temporal neocortex (Wennberg et al., 2011). In this study EEG source imaging (ESI) was performed on 64 identical right anterior temporal spikes (and 48 homologous left anterior temporal spikes) in a patient with MTLE investigated with simultaneous depth and subdural intracranial EEG and 27 channel scalp EEG. The effects of different realistic forward models, low frequency filters (LFFs) and spike averaging were assessed in terms of the reliability and physiologic validity of the source solutions. ResultsDipole mapping and distributed source modeling solutions for the grand average of all spikes were accurately localized to the superficial anterolateral temporal neocortex within 1cm of the intracranially defined spike generator, irrespective of forward model or LFF. ESI of single spikes, however, showed poor reliability (i.e., dissimilar localization results for intracranially identical spikes). Even with an optimal combination of individualized volume conductor and 3Hz LFF more than one third of single spike source solutions were physiologically invalid. Spike averaging, especially of 8 or more spikes, significantly increased the proportion of valid source solutions. ConclusionsESI of individual anterior temporal spikes was limited by low reliability and a high likelihood of physiologically invalid source solutions. Spike averaging of 8 or more identical spikes prior to ESI, however, reliably produced accurate source solutions localized to the anterolateral temporal neocortex. SignificanceESI performed on averages of identical spikes can provide highly accurate noninvasive source localization of the anterolateral temporal neocortical region responsible for generating classical anterior temporal lobe spikes. The reliability and validity of ESI performed on individual spikes, however, is relatively limited.

Diagnosis of Early Alzheimer's Disease Based on EEG Source Localization and a Standardized Realistic Head Model

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta band of AD subjects shows an increase in the power, whereas the alpha band shows a decreased activity (P-value <0.05). In the right brain hemisphere of AD subjects, a decreased activity is observed in all frequency bands. It was also noticed that the right temporal region shows a significant difference between the two groups in all frequency bands. Using a support vector machine, control and patient groups are discriminated with an accuracy of 84.4%, sensitivity 75.0%, and specificity of 93.7%.

Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials

One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations.