EEG Source Localization Research Articles

Electroencephalography source localization.

Electroencephalography (EEG) has been and is still widely used in brain function research. EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution. Furthermore, current advanced technologies enable accurate mathematical calculations and sophisticated localization from EEG data. Several important factors should be considered for EEG analysis using these advanced technologies. First, raw EEG data contain physiological or nonphysiological artifacts. Therefore, preprocessing methods and algorithms to detect and remove these artifacts have been proposed and developed. In the analysis of preprocessed EEG, forward and inverse problems require solving and several proposed models have been applied. To solve the forward problem, the source information and matrix parameters from which the EEG originates are essential. Therefore, an accurate head model is required. In contrast, the possible combinations of the current sources computed inversely from EEG measured at a limited number of electrodes are infinite, referring to the inverse problem. The inverse problem can be solved by setting limits based on assumptions made of the anatomy and physiology on the generation and propagation of the current sources. Thus, methods such as dipole source models and distributed source models have been proposed. Source localization requires the consideration of many factors such as the preprocessing of raw EEG data, artifact removal, accurate head models and forward problems, and inverse computation problems. This review summarizes the methods and considerations applied to the above EEG source localization process. It also introduces the applications of EEG source localization for epilepsy and other diseases as well as brain function studies and discusses future directions.

Topological and dynamical features of source localized EEG networks in presaccadic visual processing

Topological and dynamical features of source localized EEG networks in presaccadic visual processing

Novel advances in the characterization of dementia subtypes: from multi‐feature classification to whole brain computational models

AbstractBackgroundBrain functional connectivity analyses derived from electroencephalography (EEG) provides relevant information for classification of dementia subtypes. The predictive strength of classification tools can be benefit from integrative, multi‐feature analysis of EEG which result in composite metric of functional connectivity. Additionally, significant improvement can be obtained when connectivity analyses consider the dependence between groups of regions as a whole system (high order interactions), instead of conceiving the network as a collection of pairwise interactions.MethodFive minutes, resting‐state EEG (rsEEG) was recorded from healthy controls (HC, n=42), and participants diagnosed with either behavioral variant frontotemporal dementia (bvFTD, n=19) or Alzheimer Disease (AD, n=33). EEG source localization analyses were conducted. Whole brain functional connectivity (82 anatomic compartments, AAL atlas) was analyzed using 17 metrics to capture different statistical dependence between source space rsEEG time series, in both time‐, and frequency‐domains. Furthermore, high‐order, non‐lineal statistical interdependencies between group of brain regions were assessed using multivariate information theory. Gradient boosting classifiers with Bayesian optimization, harmonization, and feature elimination were implemented.ResultClassification of dementia subtypes, using a particular frequency‐domain connectivity metric, displayed better performances as features for classification comprised information from multiple EEG frequency bands. Likewise, classification systems including information from several connectivity metrics outperformed those achieved with individual metrics. Dementia classification conducted with connectivity metrics derived from mutual information significantly improved as order of interaction (number of brain regions consider as a network) increased. This latter approach boosted multimodal (fMRI‐EEG) classification of dementias based on brain functional connectivity, and served as input for whole‐brain computational models describing the pathophysiology of neurodegenerative diseases.ConclusionIntegrative, composite metrics of connectivity, and connectivity analyses of source localized rsEEG based in high order interactions capture relevant information to discriminate dementia subtypes, and provide a reliable and interpretable description of brain functional connectivity and neurodegeneration.

The effect of low-frequency rTMS on auditory hallucinations, EEG source localization and functional connectivity in schizophrenia

BackgroundLow-frequency repetitive transcranial magnetic stimulation (LF-rTMS) diminishes auditory hallucinations (AHs). The aims of our study were a) to assess the efficacy of LF-rTMS in a randomized, sham-controlled double-blind alignment, b) to identify the electrophysiological changes accompanying the LF-rTMS, and c) to identify the influence of LF-rTMS on brain functional connectivity (FC). MethodsNineteen schizophrenia patients with antipsychotic-resistant AHs were randomized to either active (n = 10) or sham (n = 9) LF-rTMS administered over the left temporo-parietal region for ten days. The clinical effect was assessed by the Auditory Hallucination Rating Scale (AHRS). The localization of the differences in electrical activity was identified by standardized low resolution brain electromagnetic tomography (sLORETA) and FC was measured by lagged phase synchronization. ResultsAHRS scores were significantly improved for patients receiving active rTMS compared to the sham (median reduction: 40 % vs 12 %; p = 0.01). sLORETA revealed a decrease of alpha-2, beta-1,-2 bands in the left hemisphere in the active group. Active rTMS led to a decrease of the lagged phase connectivity in beta bands originating in areas close to the site of stimulation, and to a prevailing increase of alpha-2 FC. No significant differences in current density or FC were observed in the sham group. LimitationsLimitations to our study included the small group sizes, and the disability of LORETA to assess subcortical neuronal activity. ConclusionsLF-rTMS attenuated AHs and induced a decrease of higher frequency bands on the left hemisphere. The FC changes support the assumption that LF-rTMS is linked to the modulation of cortico-cortical coupling.

The development of an automated machine learning pipeline for the detection of Alzheimer’s Disease

Although Alzheimer’s disease is the most prevalent form of dementia, there are no treatments capable of slowing disease progression. A lack of reliable disease endpoints and/or biomarkers contributes in part to the absence of effective therapies. Using machine learning to analyze EEG offers a possible solution to overcome many of the limitations of current diagnostic modalities. Here we develop a logistic regression model with an accuracy of 81% that addresses many of the shortcomings of previous works. To our knowledge, no other study has been able to solve the following problems simultaneously: (1) a lack of automation and unbiased removal of artifacts, (2) a dependence on a high level of expertise in data pre-processing and ML for non-automated processes, (3) the need for very large sample sizes and accurate EEG source localization using high density systems, (4) and a reliance on black box ML approaches such as deep neural nets with unexplainable feature selection. This study presents a proof-of-concept for an automated and scalable technology that could potentially be used to diagnose AD in clinical settings as an adjunct to conventional neuropsychological testing, thus enhancing efficiency, reproducibility, and practicality of AD diagnosis.

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

This paper is focused on optimizing EEG source localization of visual neural activities generated in the posterior lobe of the human brain. The visual and neural systems of the human brain process the captured images of faces or scenes into optical and chemical neurons in order to produce electrical potentials over the scalp surface, where EEG electrodes measure these signals to sense the underneath visual brain activity. However, it is categorically hard to localize the true neural sources in the human brain's visual cortex due to overlapping and interaction of other active areas of the lobes of the brain. Thus, a novel algorithm of MSP inversion-based Bayesian framework with varying patches for providing the optimal free energy and minimum location in the visual cortex is proposed to address this issue. This algorithm is integrated with a synthetic EEG dataset generation scheme to validate active neural sources. This proposed algorithm provides satisfactory results in terms of optimal free energy with minimum localization error and validates true active sources in the brain. The SPM12 Toolbox is applied in processing the visual EEG dataset in this research. The application of this proposed algorithm is beneficial in terms of localizing the optimum visual sources and identifying the visual disorders or diseases in the human brain.

The importance of resource allocation for the interplay between automatic and cognitive control in response inhibition – An EEG source localization study

Goal-directed behavior often requires the inhibition of prepotent automatic responses. Response inhibition encompasses several top-down cognitive operations embedded in a neural network extending from fronto-cortical regions to subcortical nuclei. Yet, it has remained unclear whether the early allocation of cognitive resources also modulates response inhibition performance and neural structures involved in this process. To investigate this question, we used a Simon Nogo task, which was designed to manipulate the relationship between automaticity and cognitive control during response inhibition, and combined it with an electroencephalogram (EEG) and source localization approach. We showed that the early allocation of cognitive resources, as reflected by the P2 amplitude, might be a critical determinant in the interplay between automaticity and cognitive control in response inhibition. Specifically, the obtained results demonstrated that individual variations in cognitive resource allocation modulated the need for conflict monitoring and engagement in cognitive control processes, as reflected by N2 and P3b amplitudes, respectively. Importantly, larger P2 amplitudes were associated with higher activation in cortical regions encompassing the temporo-parietal junction (TPJ). This stresses the importance of this cortical region for the encoding of relevant stimulus information to resolve conflicting contexts in response inhibition. The increased cognitive control in more automatic contexts was also reflected by higher activation of the superior and medial frontal cortices. These findings provide a new perspective on response inhibition, suggesting that early resource allocation is a central modulator of the interaction between automaticity and cognitive control.

Evaluation of skull conductivity using SCALE head tissue conductivity estimation using EEG.

Inaccurate estimation of skull conductivity is the largest impediment to high-resolution EEG source imaging because of its strong influence and wide variability across individuals. Nonetheless, there is yet no widely applied method for noninvasively measuring individual skull conductivity. We presented a skull conductivity and source location estimation algorithm (SCALE) for simultaneously estimating skull conductivity and the cortical distributions of 18-20 effective sources derived from the EEG data by independent component analysis (ICA). SCALE combines a realistic Finite Element Method (FEM) head model built from a magnetic resonance (MR) head image with the effective source scalp maps to estimate brain-to-skull conductivity ratio (BSCR) and to map the effective sources on the cortical surface. To estimate the robustness of SCALE BSCR estimates, we applied SCALE to MR image and high-density EEG data from ten participants, five having data from 2-3 different tasks and sessions. As expected, across participants SCALE BSCR estimates differed widely (mean 32.8, range 18-78). Within-participant SCALE BSCR estimates were far more consistent than between participants. By incorporating SCALE-optimized distributed EEG source localization, stable functional imaging of cortical EEG effective sources can become routine, giving relatively low-cost EEG imaging a spatial resolution compatible with other brain imaging results and uniquely capable for studying brain dynamics supporting thought and action in laboratory, virtual, and natural environments.

Performance Analysis of Low-Resolution Electroencephalogram Source Localization Techniques

Brain source localization has attained significant fascination over the last few decades. Source localization in the human brain is a prospective complication that is derived in the multifaceted real-world complications because of the brain’s practical and biological density, other than medical precincts of assembling Electroencephalogram (EEG) from enormously various subjects. It is validated that the electromagnetic signal recorded on the top of the scalp is owing to the collective actions of neurons inside the brain. Any impulsive action of the brain, sensory stimulus, cognitive action, or the generation of motor yield possibly will offer intensification to such neuronal actions. Source- localization in the human brain implicates the localization and detection of such primary neuronal originators into the brain. Although renowned and different research in the area, the complications remnants to be a mysterious inverse problem in the brain signal processing research. The performance of EEG source localization techniques based on standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) and exact Low-Resolution Brain Electromagnetic Tomography (eLORETA) is highlighted in this research. The Event-Related Potential (ERP) records with chromatic stimulus are considered for analysis at diverse time intervals for both techniques and final results are discussed in reports of scalp map, slice view, and, cortex map and proposed the optimum techniques for EEG source localization.

Electrocortical correlates of the association between cardiorespiratory fitness and sustained attention in young adults

Cardiorespiratory fitness is thought to be positively related to sustained attention. However, the underlying mechanisms of this relationship have yet to be fully elucidated. The objective of this study was to i) explore the relationship between cardiorespiratory fitness and sustained attention in 72 young adults (18–37 years old) and ii) provide insight on the electrocortical dynamics supporting sustained attention performance in individuals differing in cardiorespiratory fitness by means of EEG topographic analyses and source localization. Behaviorally, cardiorespiratory fitness was related to faster response times and higher accuracy in the psychomotor vigilance task even when adjusting the model with confounding variables such as age, body mass index and chronic physical activity. However, there was no relationship between cardiorespiratory fitness and the classic vigilance decrement observed in the sustained attention task. At the electrocortical level, higher cardiorespiratory fitness was related to increased global field power (310–333 ms poststimulus) localized in the posterior cingulate cortex (BA 30) followed by changes in scalp topographies around the P3b ERP component (413–501 ms poststimulus), which corresponded to earlier activation of the supplementary motor areas (BA 6). This is the first study using high-density EEG, which harnesses the whole spatiotemporal dynamics of the relationship between cardiorespiratory fitness and sustained attention in young adults.

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced randomized multiresolution scanning (RAMUS) source space decomposition approach to reduce modelling errors, respectively. In particular, we aim to generalize the previously extensively used conditionally Gaussian prior (CGP) formalism to achieve distributional reconstructions with higher focality. For this purpose, we introduce as a hierarchical prior, a general exponential distribution, which we refer to as conditionally exponential prior (CEP). The first-degree CEP corresponds to focality enforcing Laplace prior, but it also suffers from strong depth bias, when applied in numerical modelling, making the deep activity unrecoverable. We sample over multiple resolution levels via RAMUS to reduce this bias as it is known to depend on the resolution of the source space. Moreover, we introduce a procedure based on the physiological a priori knowledge of the brain activity to obtain the shape and scale parameters of the gamma hyperprior that steer the CEP. The posterior estimates are calculated using iterative statistical methods, expectation maximization and iterative alternating sequential algorithm, which we show to be algorithmically similar and to have a close resemblance to the iterative ell _1 and ell _2 reweighting methods. The performance of CEP is compared with the recent sampling-based dipole localization method Sequential semi-analytic Monte Carlo estimation (SESAME) in numerical experiments of simulated somatosensory evoked potentials related to the human median nerve stimulation. Our results obtained using synthetic sources suggest that a hybrid of the first-degree CEP and RAMUS can achieve an accuracy comparable to the second-degree case (CGP) while being more focal. Further, the proposed hybrid is shown to be robust to noise effects and compares well with the dipole reconstructions obtained with SESAME.

316 Frontal-cerebellar EEG source localization and functional connectivity as predictors of Alzheimer’s disease-related cognitive decline

OBJECTIVES/GOALS: Novel EEG source localization and functional connectivity will assess frontal and cerebellar activity as predictors of subsequent memory and executive functioning (EF) decline in healthy, asymptomatic older adults who carry the Apolipoprotein-E ε4 allele (ε4+), which conveys up to 12x increased risk for Alzheimers disease (AD). METHODS/STUDY POPULATION: Healthy, cognitively intact ε4+ older adults (n = 23; ages 65-89) completed neuropsychological testing (focus on memory and EF) and EEG at Time 1, returning an average of one year later for neuropsychological retesting. EEG data during successful stop-signal inhibitory control trials will be used for advanced source localization and functional connectivity, with a focus on frontal and cerebellar regions of interest (ROIs). Source analyses will focus on the N200 time window (~200-350ms) to assess conflict processing and P300 (~300-550ms) for performance evaluation. Connectivity analyses are frequency-based, and will focus on theta band connectivity to assess conflict processing and delta to assess performance evaluation. RESULTS/ANTICIPATED RESULTS: Using hierarchical linear regression models, the magnitude of source activation within ROIs and connectivity metrics between ROIs will be used to predict residualized change in memory and executive function metrics between Time 1 and Time 2. We anticipate that 1) greater, compensatory activation in frontal ROIs during the N200 window, and 2) less cerebellar activation during P300, will predict memory and executive decline over the retest interval. Decline will also be predicted by 3) greater inter-hemispheric frontal connectivity in the theta band (conflict processing) and 4) less frontal-cerebellar delta connectivity (performance evaluation). DISCUSSION/SIGNIFICANCE: At most, ~50% of ε4 carriers will develop AD. Thus, identifying which carriers will decline is crucial to enabling successful, early intervention. Cerebellar dysfunction and impaired connectivity may be among the earliest indicators of incipient AD. Cutting-edge cerebellar EEG may enable an accessible option for early discernment of AD risk.

Individual Differences in Multisensory Processing Are Related to Broad Differences in the Balance of Local versus Distributed Information.

The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy young adults completed an audiovisual simultaneity judgment task to measure their temporal binding window (TBW), which quantifies the ability to distinguish fine discrepancies in timing between auditory and visual stimuli. EEG was then recorded during a second run of the simultaneity judgment task, and partial least squares was used to relate individual differences in the TBW width to source-localized EEG measures of local entropy and mutual information, indexing local and distributed processing of information, respectively. The narrowness of the TBW, reflecting more effective multisensory processing, was related to a broad pattern of higher mutual information and lower local entropy at multiple timescales. Furthermore, a small group of temporal and frontal cortical regions, including those previously implicated in multisensory integration and response selection, respectively, played a prominent role in this pattern. Overall, these findings suggest that individual differences in multisensory processing are related to widespread individual differences in the balance of distributed versus local information processing among a large subset of brain regions, with more distributed information being associated with more effective multisensory processing. The balance of distributed versus local information processing may therefore be a useful measure for exploring individual differences in multisensory processing, its relationship to higher cognitive traits, and its disruption in neurodevelopmental disorders and clinical conditions.

Functional and structural brain network correlates of visual hallucinations in Lewy body dementia.

Visual hallucinations are a common feature of Lewy body dementia. Previous studies have shown that visual hallucinations are highly specific in differentiating Lewy body dementia from Alzheimer’s disease dementia and Alzheimer–Lewy body mixed pathology cases. Computational models propose that impairment of visual and attentional networks is aetiologically key to the manifestation of visual hallucinations symptomatology. However, there is still a lack of experimental evidence on functional and structural brain network abnormalities associated with visual hallucinations in Lewy body dementia.We used EEG source localization and network based statistics to assess differential topographical patterns in Lewy body dementia between 25 participants with visual hallucinations and 17 participants without hallucinations. Diffusion tensor imaging was used to assess structural connectivity between thalamus, basal forebrain and cortical regions belonging to the functionally affected network component in the hallucinating group, as assessed with network based statistics. The number of white matter streamlines within the cortex and between subcortical and cortical regions was compared between hallucinating and not hallucinating groups and correlated with average EEG source connectivity of the affected subnetwork. Moreover, modular organization of the EEG source network was obtained, compared between groups and tested for correlation with structural connectivity.Network analysis showed that compared to non-hallucinating patients, those with hallucinations feature consistent weakened connectivity within the visual ventral network, and between this network and default mode and ventral attentional networks, but not between or within attentional networks. The occipital lobe was the most functionally disconnected region. Structural analysis yielded significantly affected white matter streamlines connecting the cortical regions to the nucleus basalis of Meynert and the thalamus in hallucinating compared to not hallucinating patients. The number of streamlines in the tract between the basal forebrain and the cortex correlated with cortical functional connectivity in non-hallucinating patients, while a correlation emerged for the white matter streamlines connecting the functionally affected cortical regions in the hallucinating group.This study proposes, for the first time, differential functional networks between hallucinating and not hallucinating Lewy body dementia patients, and provides empirical evidence for existing models of visual hallucinations. Specifically, the outcome of the present study shows that the hallucinating condition is associated with functional network segregation in Lewy body dementia and supports the involvement of the cholinergic system as proposed in the current literature.

Altered Pain Processing Associated with Administration of Dopamine Agonist and Antagonist in Healthy Volunteers.

Striatal dopamine dysfunction is associated with the altered top-down modulation of pain processing. The dopamine D2-like receptor family is a potential substrate for such effects due to its primary expression in the striatum, but evidence for this is currently lacking. Here, we investigated the effect of pharmacologically manipulating striatal dopamine D2 receptor activity on the anticipation and perception of acute pain stimuli in humans. Participants received visual cues that induced either certain or uncertain anticipation of two pain intensity levels delivered via a CO2 laser. Rating of the pain intensity and unpleasantness was recorded. Brain activity was recorded with EEG and analysed via source localisation to investigate neural activity during the anticipation and receipt of pain. Participants completed the experiment under three conditions, control (Sodium Chloride), D2 receptor agonist (Cabergoline), and D2 receptor antagonist (Amisulpride), in a repeated-measures, triple-crossover, double-blind study. The antagonist reduced an individuals’ ability to distinguish between low and high pain following uncertain anticipation. The EEG source localisation showed that the agonist and antagonist reduced neural activations in specific brain regions associated with the sensory integration of salient stimuli during the anticipation and receipt of pain. During anticipation, the agonist reduced activity in the right mid-temporal region and the right angular gyrus, whilst the antagonist reduced activity within the right postcentral, right mid-temporal, and right inferior parietal regions. In comparison to control, the antagonist reduced activity within the insula during the receipt of pain, a key structure involved in the integration of the sensory and affective aspects of pain. Pain sensitivity and unpleasantness were not changed by D2R modulation. Our results support the notion that D2 receptor neurotransmission has a role in the top-down modulation of pain.

Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study.

A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity of BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy of a somatosensory BCI based on sensory imagery (SI). During the SI tasks, subjects were instructed to imagine a tactile sensation and to maintain the attention on the corresponding hand, as if there was tactile stimulus on the skin of the wrist. The performance across 106 healthy subjects in left- and right-hand SI discrimination was 78.9±13.2%. In 70.7% of the subjects the performance was above 70%. The SI task induced a contralateral cortical activation, and high-density EEG source localization showed that the real tactile stimulation and imagined tactile stimulation shared similar cortical activations within the somatosensory cortex. The somatosensory BCI based on SI provides a new signal modality for independent BCI development. Moreover, a combination of SI and other BCI modalities, such as motor imagery, may provide new avenues for further improving BCI usage and applicability, especially in those subjects unable to attain adequate BCI control with conventional BCI modalities.

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process.

Linear distributed inverse solutions for interictal EEG source localisation

ObjectiveTo compare the spatial accuracy of 6 linear distributed inverse solutions for EEG source localisation of interictal epileptic discharges: Minimum Norm, Weighted Minimum Norm, Low-Resolution Electromagnetic Tomography (LORETA), Local Autoregressive Average (LAURA), Standardised LORETA, and Exact LORETA. MethodsSpatial accuracy was assessed clinically by retrospectively comparing the maximum source of averaged interictal discharges to the resected brain area in 30 patients with successful epilepsy surgery, based on 204-channel EEG. Additionally, localisation errors of the inverse solutions were assessed in computer simulations, with different levels of noise added to the signal in both sensor space and source space. ResultsIn the clinical evaluations, the source maximum was located inside the resected brain area in 50–57% of patients when using LORETA or LAURA, while all other inverse solutions performed significantly worse (17–30%; corrected p < 0.01). In the simulation studies, when noise levels exceeded 10%, LORETA and LAURA had substantially smaller localisation errors than the other inverse solutions. ConclusionsLORETA and LAURA provided the highest spatial accuracy both in clinical and simulated data, alongside with a comparably high robustness towards noise. SignificanceAmong the different linear inverse solution algorithms tested, LORETA and LAURA might be preferred for interictal EEG source localisation.

Sparse EEG source localization in frequency domain.

This work presents an approach for EEG source localization when strong priors on predominant frequencies in the activities of the source are available. We describe the fundamentals of the used source reconstruction method based on a greedy approach, which can be applied indifferently in the time or frequency domain. The method is evaluated using simulated data reproducing realistic recorded activities in the context of fast periodic visual stimulation. In particular the advantage of reconstructing the source in the frequency domain against time domain is quantified in a realistic setup. Finally, the performances of the method are illustrated on real EEG signals recorded during a fast periodic visual stimulation task.