Estimation Of Current Density Research Articles

Kernel density smoothing of composite spatial data on administrative area level

Composite spatial data on administrative area level are often presented by maps. The aim is to detect regional differences in the concentration of subpopulations, like elderly persons, ethnic minorities, low-educated persons, voters of a political party or persons with a certain disease. Thematic collections of such maps are presented in different atlases. The standard presentation is by Choropleth maps where each administrative unit is represented by a single value. These maps can be criticized under three aspects: the implicit assumption of a uniform distribution within the area, the instability of the resulting map with respect to a change of the reference area and the discontinuities of the maps at the borderlines of the reference areas which inhibit the detection of regional clusters.In order to address these problems we use a density approach in the construction of maps. This approach does not enforce a local uniform distribution. It does not depend on a specific choice of area reference system and there are no discontinuities in the displayed maps. A standard estimation procedure of densities are Kernel density estimates. However, these estimates need the geo-coordinates of the single units which are not at disposal as we have only access to the aggregates of some area system. To overcome this hurdle, we use a statistical simulation concept. This can be interpreted as a Simulated Expectation Maximisation (SEM) algorithm of Celeux et al (1996). We simulate observations from the current density estimates which are consistent with the aggregation information (S-step). Then we apply the Kernel density estimator to the simulated sample which gives the next density estimate (E-Step).This concept has been first applied for grid data with rectangular areas, see Groß et al (2017), for the display of ethnic minorities. In a second application we demonstrated the use of this approach for the so-called “change of support” (Bradley et al 2016) problem. Here Groß et al (2020) used the SEM algorithm to recalculate case numbers between non-hierarchical administrative area systems. Recently Rendtel et al (2021) applied the SEM algorithm to display spatial-temporal clusters of Corona infections in Germany.Here we present three modifications of the basic SEM algorithm: 1) We introduce a boundary correction which removes the underestimation of kernel density estimates at the borders of the population area. 2) We recognize unsettled areas, like lakes, parks and industrial areas, in the computation of the kernel density. 3) We adapt the SEM algorithm for the computation of local percentages which are important especially in voting analysis.We evaluate our approach against several standard maps by means of the local voting register with known addresses. In the empirical part we apply our approach for the display of voting results for the 2016 election of the Berlin parliament. We contrast our results against Choropleth maps and show new possibilities for reporting spatial voting results.

TMS‐induced EEG perturbation as a marker of psychological resilience to deleterious mental health effects during the COVID‐19 pandemic

BackgroundSocial, economic and psychological hardships associated with the COVID‐19 pandemic are expected to result in a global burden on mental health outcomes. However, while some individuals suffer from increasing distress and reduced quality of life, others will show no negative effects. A better understanding of brain mechanisms subtending resilience would be helpful in informing future recommendations to individuals and societies facing the present pandemic and future similar events. Here, we compared neurophysiological brain markers between individuals who exhibited resilience or vulnerability to pandemic associated psychological stress.Method23 participants from the longitudinal study cohort of the Barcelona Brain Health Initiative (Cattaneo et al., Front. Aging Neurosci. 2018;10:321) who underwent concurrent transcranial magnetic stimulation with electroencephalography (TMS‐EEG), were classified as either resilient (n=16) or non‐resilient (n=7), based on their scoring in the PHQ‐4 questionnaire (Kroenke et al., Psychosomatics 2009;50(6):613–21) along four timepoints; one before COVID‐19 outbreak and three spanning 2.5 months during the pandemic. Individuals maintaining a score below 3 across all timepoints were deemed resilient, while those scoring below 3 before pandemic but higher than 2 at any pandemic timepoint were considered non‐resilient. TMS‐EEG data was collected by delivering 120 single TMS pulses to the dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL). TMS evoked global mean field amplitude and local response at the stimulation site were computed.ResultFigure 1 depicts time‐series for DLPFC and IPL responses for both groups of subjects. Overall, non‐resilient individuals exhibited a larger global response to TMS perturbation during DLPFC stimulation, as well as larger local current density estimates during IPL stimulation.ConclusionThese preliminary results revealed that non‐resilient individuals were more susceptible to TMS perturbation, shown by global DLPFC and local IPL reactivity. Notably, these targets are nodes of the default mode and cognitive control networks affected by stress (van Oort et al., Neurosci. Biobehav. Rev. 2017;83:281–97), and the left frontal cortex has been proposed as a cognitive resilience hub (Franzmeier et al., J. Alzheimer’s Dis. 2017;59(4):1381–92.). Future studies should investigate and confirm the possibility that these nodes constitute a shared neurophysiological substrate for psychological and cognitive resilience.

Multifocal Transcranial Direct Current Stimulation Modulates Resting-State Functional Connectivity in Older Adults Depending on the Induced Current Density.

Combining non-invasive brain stimulation (NIBS) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising approach to characterize and potentially optimize the brain networks subtending cognition that changes as a function of age. However, whether multifocal NIBS approaches are able to modulate rs-fMRI brain dynamics in aged populations, and if these NIBS-induced changes are consistent with the simulated electric current distribution on the brain remains largely unknown. In the present investigation, thirty-one cognitively healthy older adults underwent two different multifocal real transcranial direct current stimulation (tDCS) conditions (C1 and C2) and a sham condition in a crossover design during a rs-fMRI acquisition. The real tDCS conditions were designed to electrically induce two distinct complex neural patterns, either targeting generalized frontoparietal cortical overactivity (C1) or a detachment between the frontal areas and the posteromedial cortex (C2). Data revealed that the two tDCS conditions modulated rs-fMRI differently. C1 increased the coactivation of multiple functional couplings as compared to sham, while a smaller number of connections increased in C1 as compared to C2. At the group level, C1-induced changes were topographically consistent with the calculated electric current density distribution. At the individual level, the extent of tDCS-induced rs-fMRI modulation in C1 was related with the magnitude of the simulated electric current density estimates. These results highlight that multifocal tDCS procedures can effectively change rs-fMRI neural functioning in advancing age, being the induced modulation consistent with the spatial distribution of the simulated electric current on the brain. Moreover, our data supports that individually tailoring NIBS-based interventions grounded on subject-specific structural data might be crucial to increase tDCS potential in future studies amongst older adults.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data.We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Multispacecraft Current Density Estimates in the Low‐ and Mid‐Latitude F‐Region Ionosphere Using the Swarm Constellation

AbstractWe present a new application of a multispacecraft method which provides estimates of the full electric current density vector in the ionospheric low‐ and mid‐latitude F region. The method uses the three satellites of the Swarm constellation in configurations when they were close to each other. The current density is calculated inside triangular prisms defined by the satellite positions. The technique is inspired by similar approaches such as the curlometer. Here, we propose an alternative mathematical treatment, which involves the use of the well‐known curl‐B technique and a least squares minimization. The proposed formalism provides a rigorous way to propagate the errors through the problem and special care is taken to validate the algorithm and assess the relevance of the derived currents. Such techniques are powerful tools to study the F‐region ionosphere currents as they naturally exclude all the other contributions outside the considered volumes. The F region is known to host currents with a complex spatiotemporal variability. Besides the intrinsic interest of this system itself, understanding the dynamics of these currents is also critical for global geomagnetic field modeling as they are an important source of error in the models. In this paper, we present the full algorithm and test the method on both synthetic and real Swarm satellite data on February 15, 2014. Several sources of error are investigated. Additionally, the results reveal expected characteristics of F‐region interhemispheric field‐aligned currents. Some new characteristics of F‐region currents, potentially associated with dynamo and pressure currents, are also observed.

Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback

Research and design of virtual reality technologies with mental-health focused applications has increased dramatically in recent years. However, the applications and psychological outcomes of augmented reality (AR) technologies still remain to be widely explored and evaluated. This is particularly true for the use of AR for the self-management of stress, anxiety, and mood. In the current study, we examined the impact of a brief open heart meditation AR experience on participants with moderate levels of anxiety and/or depression. Using a randomized between-group design subjects participated in the AR experience or the AR experience plus frontal gamma asymmetry neurofeedback integrated into the experience. Self-reported mood state and resting-state EEG were recorded before and after the AR intervention for both groups. Participants also reported on engagement and perceived use of the experience as a stress and coping tool. EEG activity was analyzed as a function of the frontal, midline, and parietal scalp regions, and with sLORETA current source density estimates of anterior cingulate and insular cortical regions of interest. Results demonstrated that both versions of the AR meditation significantly reduced negative mood and increased positive mood. The changes in resting state EEG were also comparable between groups, with some trending differences observed, in line with existing research on open heart and other loving-kindness and compassion-based meditations. Engagement was favorable for both versions of the AR experience, with higher levels of engagement reported with the addition of neurofeedback. These results provide early support for the therapeutic potential of AR-integrated meditations as a tool for the self-regulation of mood and emotion, and sets the stage for more research and development into health and wellness-promoting AR applications.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives.

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low‐resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain‐related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non‐painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

The Gas Diffusion Electrode Setup as Straightforward Testing Device for Proton Exchange Membrane Water Electrolyzer Catalysts.

Hydrogen production from renewable resources and its reconversion into electricity are two important pillars toward a more sustainable energy use. The efficiency and viability of these technologies heavily rely on active and stable electrocatalysts. Basic research to develop superior electrocatalysts is commonly performed in conventional electrochemical setups such as a rotating disk electrode (RDE) configuration or H-type electrochemical cells. These experiments are easy to set up; however, there is a large gap to real electrochemical conversion devices such as fuel cells or electrolyzers. To close this gap, gas diffusion electrode (GDE) setups were recently presented as a straightforward technique for testing fuel cell catalysts under more realistic conditions. Here, we demonstrate for the first time a GDE setup for measuring the oxygen evolution reaction (OER) of catalysts for proton exchange membrane water electrolyzers (PEMWEs). Using a commercially available benchmark IrO2 catalyst deposited on a carbon gas diffusion layer (GDL), it is shown that key parameters such as the OER mass activity, the activation energy, and even reasonable estimates of the exchange current density can be extracted in a realistic range of catalyst loadings for PEMWEs. It is furthermore shown that the carbon-based GDL is not only suitable for activity determination but also short-term stability testing. Alternatively, the GDL can be replaced by Ti-based porous transport layers (PTLs) typically used in commercial PEMWEs. Here a simple preparation is shown involving the hot-pressing of a Nafion membrane onto a drop-cast glycerol-based ink on a Ti-PTL.

Characterization of Source-Localized EEG Activity During Sustained Deep-Tissue Pain.

Musculoskeletal pain is a clinical condition that is characterized by ongoing pain and discomfort in the deep tissues such as muscle, bones, ligaments, nerves, and tendons. In the last decades, it was subject to extensive research due to its high prevalence. Still, a quantitative description of the electrical brain activity during musculoskeletal pain is lacking. This study aimed to characterize intracranial current source density (CSD) estimations during sustained deep-tissue experimental pain. Twenty-three healthy volunteers received three types of tonic stimuli for three minutes each: computer-controlled cuff pressure (1) below pain threshold (sustained deep-tissue no-pain, SDTnP), (2) above pain threshold (sustained deep-tissue pain, SDTP) and (3) vibrotactile stimulation (VT). The CSD in response to these stimuli was calculated in seven regions of interest (ROIs) likely involved in pain processing: contralateral anterior cingulate cortex, contralateral primary somatosensory cortex, bilateral anterior insula, contralateral dorsolateral prefrontal cortex, posterior parietal cortex and contralateral premotor cortex. Results showed that participants exhibited an overall increase in spectral power during SDTP in all seven ROIs compared to both SDTnP and VT, likely reflecting the differences in the salience of these stimuli. Moreover, we observed a difference is CSD due to the type of stimulus, likely reflecting somatosensory discrimination of stimulus intensity. These results describe the different contributions of neural oscillations within these brain regions in the processing of sustained deep-tissue pain.

Evaluating Current Density Modeling of Non-Invasive Eye and Brain Electrical Stimulation Using Phosphene Thresholds.

Because current flow cannot be measured directly in the intact retina or brain, current density distribution models were developed to estimate it during magnetic or electrical stimulation. A paradigm is now needed to evaluate if current flow modeling can be related to physiologically meaningful signs of true current distribution in the human brain. We used phosphene threshold measurements (PTs) as surrogate markers of current-flow to determine if PTs, evoked by transcranial alternating current stimulation (tACS), can be matched with current density estimates generated by head model-based computer simulations. Healthy, male subjects (n=15) were subjected to three-staged PT measurements comparing six unilateral and one bilateral stimulation electrode montages according to the 10/20 system: Fp2-Suborbital right (So), Fp2-right shoulder (rS), Fp2-Cz, Fp2- O2, So-rS, Cz-F8 and F7-F8. The stimulation frequency was set at 16 Hz. Subjects were asked to report the appearance and localization of phosphenes in their visual field for every montage. Current density models were built using multi-modal imaging data of a standard brain, meshed with isotropic conductivities of different tissues of the head using the SimBio and SCIRun software packages. We observed that lower PTs were associated with higher simulated current levels in the unilateral montages of the model head, and shorter electrode distances to the eye had lower PTs. The lowest mean PT and the lowest variability were found in the F7-F8 montage (95±33 μA). Our results confirm the hypothesis that phosphenes are primarily of retinal origin, and they provide the first in vivo evidence that computer models of current flow using head models are a valid tool to estimate real current flow in the human eye and brain.

Estimation of the electric current density in the Gulf of California induced by the M2 tidal current

The electric current density generated in the Gulf of California by the interaction between the ocean currents produced by the M2 tide and the geomagnetic field was calculated in this study. The value of the geomagnetic field in the Gulf of California was determined from World Magnetic Model 2010 data; the tidal velocities of the seawater were calculated using a three–dimensional hydrodynamic–numerical model forced by the M2 tide with a horizontal cell resolution of 3’ × 3’ and 12 vertical layers. The results showed that the electric current density is maximum during the fastest flood and ebb currents; it exhibits sinusoidal behavior, and the largest amplitudes always appear in the upper layer. The electric field and the electric potential difference exhibit similar behaviors. Important areas for the generation of electric fields are in the upper Gulf of California, Colorado River Delta, archipelago region, and Ballenas and Salsipuedes channels.

Finite Element Method Based Estimation of Critical Current Density of NbTi

The purpose of this article is to estimate the magnetic field dependent critical current density scaling relation of NbTi used in a superconducting wire. The estimation problem is set using a finite element method based forward model solving the critical current of a wire in external magnetic fields. A sensitivity analysis is carried out to reveal the uncertainties in the process. It is shown that the methods provide accurate predictions of critical currents in a wire, but there are uncertainties related to the zero-field critical current density of the material.

Current estimates and vegetation preferences of an endangered population of the vulnerable greater glider at Seven Mile Beach National Park

AbstractThis study aimed to provide accurate and current distribution and density estimates of the greater glider population size at Seven Mile Beach National Park, New South Wales Australia from extensive spotlight surveys. Distribution mapping of greater gliders and ArcGIS modelling allowed effective visual representation of the population’s distribution and provided preliminary evidence of a vegetation preference for the species within the study area. Spotlight surveys revealed the population ranged from 0 to 1.13 (±0.34) greater gliders per hectare; with an average of 0.41 greater gliders per hectare across Seven Mile National Park. An estimated total of 335 greater gliders could reside in the area. Vegetation surveys confirmed there was a higher encounter rate of greater gliders within Eucalyptus pilularis, blackbutt dominated forest. As expected, the number of suitable hollows was also an important factor affecting greater glider distribution. These findings have site‐specific implications for a number of suitable management options (e.g. nest boxes, road crossing structures and glider poles).

Effects of cost surface uncertainty on current density estimates from circuit theory.

BackgroundConservation practitioners are often interested in developing land use plans that increase landscape connectivity, which is defined as the degree to which the landscape facilitates or impedes movement among resource patches. Landscape connectivity is often estimated with a cost surface that indicates the varying costs experienced by an organism in moving across a landscape. True, or absolute costs are rarely known however, and therefore assigning costs to different landscape elements is often a challenge in creating cost surface maps. As such, we consider it important to understand the sensitivity of connectivity estimates to uncertainty in cost estimates.MethodsWe used simulated landscapes to test the sensitivity of current density estimates from circuit theory to varying relative cost values, fragmentation, and number of cost classes (i.e., thematic resolution). Current density is proportional to the probability of use during a random walk. Using Circuitscape software, we simulated electrical current between pairs of nodes to create current density maps. We then measured the correlation of the current density values across scenarios.ResultsIn general, we found that cost values were highly correlated across scenarios with different cost weights (mean correlation ranged from 0.87 to 0.92). Changing the spatial configuration of landscape elements by varying the degree of fragmentation reduced correlation in current density across maps. We also found that correlations were more variable when the range of cost values in a map was high.DiscussionThe low sensitivity of current density estimates to relative cost weights suggests that the measure may be reliable for land use applications even when there is uncertainty about absolute cost values, provided that the user has the costs correctly ranked. This finding should facilitate the use of cost surfaces by conservation practitioners interested in estimating connectivity and planning linkages and corridors.

Calibration of atmospheric density model based on Gaussian Processes

Neutral mass density is presently the predominant uncertain term among all the factors affecting the atmospheric drag, which is the dominant perturbation force for space objects at low altitude. The current best density estimation performance is often achieved by empirical models that can be limited by their assumed, parametric formulations of regression. This paper presents a density estimation framework that integrates information from empirical models, environment conditions, and satellite measurement data. Different from existing frameworks, the new integration mechanism is based on Gaussian Processes (GPs) which are nonlinear, non-parametric regression methods. The method can estimate both current and future densities. Furthermore, it will provide uncertainty quantifications in its estimates through GPs' underlying Bayesian inference. Simulations are designed to test the hypothesis that the new framework is valuable to improve the nowcast performance of the empirical models, and to predict future density. Empirical models including NRLMSISE-00 and JB2008 and accelerometer-inferred densities from satellite CHAMP are used for the study. The new method is shown to achieve better Pearson correlation coefficient (R), root mean squared error (RMSE), and mean ratio from the empirical models when the density estimation is tested on both the missing data and future densities, which are, respectively, within and following the GP's training period. Together with providing quality uncertainty estimations, the proposed framework has the great potential to reduce the estimation errors from the empirical model and provide an effective means to estimate density for a satellite.

Crowd Estimation Using Electromagnetic Wave Power-Level Measurements: A Proof of Concept

Current crowd density estimation technologies that leverage IR depth perception, video and image processing or WiFi/BLE-based sniffing and probing have privacy and deployment issues. This paper presents a novel method for non-intrusive crowd density estimation that monitors variation in EM radiation within an environment. The human body's electrical and magnetic characteristics can be correlated with variations in available EM energy. This allows for the determination of the number of people within a room. Simulations conducted using Comsol to analyse and measure electromagnetic energy levels inside a room containing human bodies. Experimental analysis provides validation of the simulation results by showing 0.8 dBm drop on the average level of EM energy per person.

Estimation of exchange current density for hydrogen evolution reaction of copper electrode by using the differentiating polarization method

Wide application of the Tafel extrapolation method (TEM) for the estimation value of the exchange current density (i0) on the hydrogen evolution reaction (HER) is still hindered because of some issues. For example, a possibility of improper reading of the tafel slope and the physical resistant such as an oxide film on electrode and solution resistance which requires more consideration in the calculation. The differential polarization method (DPM) was introduced as a reliable method to estimate the value of the i0. In this paper, HER of a copper electrode in 0.5 mol dm−3 H2SO4 solution was investigated and compared using both methods. The DPM shows that the undesirable physical factor of copper in the measurement could be removed rationally and is acceptable on the calculation of i0. Moreover, the tafel slope region could be read accurately.

Field Emission From Nanostructured Tendril Bundles

Field emission from tungsten surfaces with nano-tendril bundles formed after exposure to helium plasma with nitrogen or neon impurities was measured using a vacuum diode device. Field enhancement factor and effective emission area were calculated using the Fowler–Nordheim formula and compared for different types of nanostructures. The field enhancement factor for these structures can reach several thousands and the effective emission area can be more than 1000 nm2. Anode luminescence was detected using long-exposure photography and allowed for estimation of current density near the anode. Unipolar arc tracks were observed on the samples with the highest field emission intensity. We show that field emission from nano-tendril bundles is much higher than that from tungsten fuzz or pure tungsten, and fusion-relevant plasma conditions may lead to arc initiation.

Estimation of Current Density Using High-Speed-Camera Recordings in a Model Spark Gap during Surge Currents

For experimental investigations of short time plasma in spark gaps, as used in surge protective devices, high-speed camera recordings are used frequently. The analysis of these recordings provides further details regarding the plasma state and distribution. These deduced details are used to assist research and development processes. To increase the benefit of high-speed camera recordings an empirical model is presented to improve the picture analysis. In this model the recorded radiation intensity is empirically related to the current density within a spark gap. Therefore a specially adapted model spark gap was developed and tested. In this model spark gap areas with homogenous current densities occur. These current densities are determined in the experimental setup through current measurements with separated electrodes. Additionally, the relative radiation intensity between the electrodes is identified using high-speed camera recordings. An empirical correlation between these two measurement values was found and is discussed. It confirmed that the determined correlation improve the mostly intuitive interpretation of high speed camera recordings in spark gaps.

Quantitative Evaluation in Estimating Sources Underlying Brain Oscillations Using Current Source Density Methods and Beamformer Approaches.

Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA), minimum norm estimates (MNE), and beamformers such as dynamic imaging of coherent sources (DICS) and linearly constrained minimum variance (LCMV). These algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. On the other hand, there are very few comparative analyses that evaluates the “ground truth detection” capabilities of these methods. The current article evaluates the reliability in estimation of sources of spectral event generators in the cortex using a two-pronged approach. First, simulated EEG data with point dipoles and distributed dipoles are used to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive (FP) ratios while detecting already known location of neural activity generators under varying signal-to-noise ratios (SNRs). Second, empirical EEG data during auditory steady state responses (ASSRs) in human participants were used to compare the distributed nature of source localization. All methods were successful in recovery of point sources in favorable signal to noise scenarios and could achieve high hit rates if FPs are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of FPs is much superior in eLORETA. Subsequently drawbacks and strengths of each method are highlighted with a detailed discussion on how to choose a technique based on empirical requirements.