Method For EEG Source Localization Research Articles

Evaluation of Brain Source Localization Methods Based on Test-Retest Reliability With Multiple Session EEG Data.

Various EEG source localization methods have been proposed for functional brain research. The evaluation and comparison of these methods are usually based on simulated data but not real EEG data, as the ground truth of source localization is unknown. In this study, we aim to evaluate source localization methods quantitatively under the real situation. We examined the test-retest reliability of the source signals reconstructed from a public six-session EEG data of 16 subjects performing face recognition tasks by five mainstream methods, including weighted minimum norm estimation (WMN), dynamical Statistical Parametric Mapping (dSPM), Standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA), dipole modeling and linearly constrained minimum variance (LCMV) beamformers. All methods were evaluated in terms of peak localization reliability and amplitude reliability of source signals. In the two brain regions responsible for static face recognition, all methods have promising peak localization reliability, with WMN showing the smallest peak dipole distance between session pairs. The spatial stability of source localization in the familiar face condition is better than those in the unfamiliar face and the scrambled face conditions in the face recognition areas in the right hemisphere. In addition, the test-retest reliability of source amplitude by all methods is good to excellent under the familiar face condition. Stable and reliable results for source localization can be obtained in the presence of evident EEG effects. Due to different levels of a priori knowledge, different source localization methods have different applicable scenarios. These findings provide new evidence for the validity of source localization analysis and a new perspective for the evaluation of source localization methods on real EEG data.

Robust Evaluation and Comparison of EEG Source Localization Algorithms for Accurate Reconstruction of Deep Cortical Activity

Accurately reconstructing deep cortical source activity from EEG recordings is essential for understanding cognitive processes. However, currently, there is a lack of reliable methods for assessing the performance of EEG source localization algorithms. This study establishes an algorithm evaluation framework, utilizing realistic human head models and simulated EEG source signals with spatial propagations. We compare the performance of several newly proposed Bayesian algorithms, including full Dugh, thin Dugh, and Mackay, against classical methods such as MN and eLORETA. Our results, which are based on 630 Monte Carlo simulations, demonstrate that thin Dugh and Mackay are mathematically sound and perform significantly better in spatial and temporal source reconstruction than classical algorithms. Mackay is less robust spatially, while thin Dugh performs best overall. Conversely, we show that full Dugh has significant theoretical flaws that negatively impact localization accuracy. This research highlights the advantages and limitations of various source localization algorithms, providing valuable insights for future development and refinement in EEG source localization methods.

Quantitative EEG Based on Direct Recording and Source Localization Estimation in Repetitive Hand Motor Activity

Electroencephalography (EEG) has been widely used in clinical practice as a tool to guide an efficient post-stroke functional recovery program and adjust the rehabilitation training program. EEG has some advantages in temporal resolution, portability, and relatively low cost. Motor-related brain activities can be quantified by measuring the mu rhythm’s event-related desynchronization (ERD), acquired from a direct surface EEG recording. The common problem of direct surface EEG is the crosstalk between brain regions that are unrelated to the sensorimotor cortex. This problem can be solved through the EEG source localization technique. Essentially, the idea of source reconstruction is to estimate the primary cortical current densities during the given EEG recording across the head surface. The recent work aims to compare EEG power spectral density (PSD) analysis and source localization. Modification of exact-Low Resolution Brain Electromagnetic Tomography (eLORETA) was used as an EEG source localization method. EEG data recording was performed on a healthy, able-bodied male subject using 21 electrodes based on the case study’s 10-20 electrode placement system. The subject was asked to perform basic hand movements, i.e., grasping with and without hand load. Mu power was calculated from the electrodes that are located around the motor cortex (C3 and C4), while the average current density was calculated from the Brodmann Area (BA) 4 and 6, both during grasping and relaxation (baseline). The comparison showed that during grasping, there was a decrease in aver-age current density around BA-4 and BA-6 and a decrease in mu power at C3 and C4 when compared to baseline. These results indicate: (1) the activity of mu rhythm’s ERD around the sensorimotor cortex during hand movement; and (2) the possibility of using the EEG source localization method as an alternative biofeedback modality.

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization

EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the EEG, leading to an increase in localization mistakes and misdiagnoses of brain disorders. Calibration of conductivity values using uncertainty quantification (UQ) techniques is a promising approach to reduce localization errors. The widely-known UQ methods involve Bayesian approaches, which utilize prior conductivity values to derive their posterior inference and estimate their optimal calibration. However, these approaches have two significant drawbacks: solving for posterior inference is intractable, and choosing inappropriate priors may lead to increased localization mistakes. This study used the Neural Stochastic Differential equations Network (SDE-Net), a combination of dynamical systems and deep learning techniques that utilizes the Wiener process to minimize conductivity uncertainties in the VCM and improve the inverse problem. Results revealed that SDE-Net generated a lower localization error rate in the inverse problem compared to Bayesian techniques. Future studies may employ new stochastic dynamical systems-based techniques as a UQ technique to address further uncertainties in the EEG Source Localization problem. Our code can be found here: https://github.com/rrwabina/SDENet-UQ-ESL.

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.

A Novel Bayesian Approach for EEG Source Localization.

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

Higher Resolution sLORETA (HR-sLORETA) in EEG Source Imaging.

sLORETA is one of the well-established EEG source localization methods that is popular for its satisfactory estimation, simplicity, and fast computation. However, the method has a low-resolution and requires manual post-processing thresholding to provide a sparser solution with acceptable resolution in source detection. Here we propose a subspace based thresholding that results in a higher resolution brain imaging based on minimizing a desired least square source detection error. Simulation results show the proposed method, denoted by HR-sLORETA, provides stable and high resolution solution in terms of Percentage of Undetected Sources (PUS) and Spatial Dispersion (SD) compared to the existing manual thresholding approaches as well as Otsu thresholding approach. It is shown that HR-sLORETA outperforms Otsu, which is the only other available automatic thresholding method, in scenarios with three or more sources.

A New fMRI Informed Mixed-Norm Constrained Algorithm for EEG Source Localization

Complementary with electroencephalograph (EEG), functional magnetic resonance imaging (fMRI), with high spatial resolution, is powerful at providing prior source locations based on actual brain physiology. It hereby can help improve the accuracy of EEG source localization. However, most of the current methods directly penalize the sources whose fMRI activation probability is low and estimate the sources activities at every time point. Thus, they do not account for the temporal interrelated and non-stationary features of electromagnetic brain signals, and some are too much dependent on the fMRI prior. Here, we propose a new fMRI informed EEG source localization method and is termed fMRI-informed spatio-temporal unifying tomography (FIST). It uses a mixed norm constraint defined in terms of time–frequency decomposition of the sources and combines it with fMRI prior. The Fast Iterative Shrinkage Thresholding Algorithm is employed to solve the optimization problem. Both simulated and real EEG data are applied to assess the performance of the proposed method. Compared with L2-norm constrained methods, FIST has the superiority brain source estimation both in the spatial and temporal domains. By virtue of the fMRI information as a prior, FIST has great improvement in spatial accuracy and computational efficiency, when comparing with the method which only uses mixed-norm constraint. In addition, FIST shows good ability to select the fMRI priors to get a better estimation without totally depending on the prior, when comparing with the method which also has fMRI prior information.

EEG source localization using a sparsity prior based on Brodmann areas

AbstractLocalizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.

Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem.

Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have focused on spatial continuity constraints, dynamic modeling, or sparsity constraints. The combination of these key ideas could offer significant performance improvements, but substantial computational costs pose a challenge for practical application of such approaches. Here, we propose a new method for EEG source localization that combines: 1) covariance estimation for both source and measurement noises; 2) linear state-space dynamics; and 3) sparsity constraints, using 4) novel computationally efficient estimation algorithms. For source covariance estimation, we use a locally smooth basis alongside sparsity enforcing priors. For EEG measurement noise covariance estimation, we use an inverse Wishart prior density. We estimate these model parameters using an expectation-maximization algorithm that employs steady-state filtering and smoothing to expedite computations. We characterized the performance of our method by analyzing simulated data and experimental recordings of eyes-closed alpha oscillations. Our sparsity enforcing priors significantly improved estimation of both the spatial distribution and time course of simulated data, while improving computational time by more than 12-fold over previous dynamic methods. We developed and demonstrated a novel method for improved EEG source localization employing spatial covariance estimation, dynamics, and sparsity. Our approach provides substantial performance improvements over existing methods using computationally efficient algorithms that will facilitate practical applications in both neuroscience and medicine.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization.

EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.

Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model

ABSTRACTIn this paper, we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. By exploiting the internal block structure, the BSBL method solves the ill‐posed inverse problem more efficiently than other methods that do not consider block structure. Simulation experiments were conducted on a realistic head model obtained by segmentation of MRI images of the head. Two definitions of blocks were considered: Brodmann areas and automated anatomical labeling (AAL). The experiments were performed both with and without the presence of noise. Six different noise levels were considered having SNR values from 5 dB to 30 dB with 5dB increment. The evaluation reveals several potential findings—first, BSBL is more likely to produce better source localization than sparse Bayesian learning (SBL), however, this is true up until a limited number of simultaneously active areas only. Experimental results show that for 71‐channel electrodes setup BSBL outperforms SBL for up to three simultaneously active blocks. From four simultaneously active blocks SBL turns out to be marginally better and the difference between them is statistically insignificant. Second, different anatomical block structures such as Brodmann areas or AAL does not seem to produce any significant difference in EEG source localization relying on BSBL. Third, even when the block partitions are not known exactly BSBL ensures better localization than SBL as soon as block structure persists in the signal. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 46–56, 2017

Evaluation of spatial resolution and noise sensitivity of sLORETA method for EEG source localization using low-density headsets

Electroencephalography (EEG) has enjoyed considerable attention over the past century and has been applied for diagnosis of epilepsy, stroke, traumatic brain injury and other disorders where 3D localization of electrical activity in the brain is potentially of great diagnostic value. In this study we evaluate the precision and accuracy of spatial localization of electrical activity in the brain delivered by a popular reconstruction technique sLORETA applied to EEG data collected by two commonly used low-density headsets with 14 and 19 measurement channels, respectively. Numerical experiments were performed for a realistic head model obtained by segmentation of MRI images. The EEG source localization study was conducted with a simulated single active dipole, as well as with two spatially separated simultaneously active dipoles, as a function of dipole positions across the neocortex, with several different noise levels in the EEG signals registered on the scalp. The results indicate that while the reconstruction accuracy and precision of the sLORETA method are consistently high in the case of a single active dipole, even with the low-density EEG configurations considered in the present study, successful localization is much more problematic in the case of two simultaneously active dipoles. The quantitative analysis of the width of the reconstructed distributions of the electrical activity allows us to specify the lower bound for the spatial resolution of the sLORETA-based 3D source localization in the considered cases.

Evaluation of a finite-element reciprocity method for epileptic EEG source localization: Accuracy, computational complexity and noise robustness

Purpose The aim of this paper is to evaluate the performance of an EEG source localization method that combines a finite element method (FEM) and the reciprocity theorem.

Source localization using rational approximation on plane sections

In functional neuroimaging, a crucial problem is to localize active sources within the brain non-invasively, from knowledge of electromagnetic measurements outside the head. Identification of point sources from boundary measurements is an ill-posed inverse problem. In the case of electroencephalography (EEG), measurements are only available at electrode positions, the number of sources is not known in advance and the medium within the head is inhomogeneous. This paper presents a new method for EEG source localization, based on rational approximation techniques in the complex plane. The method is used in the context of a nested sphere head model, in combination with a cortical mapping procedure. Results on simulated data prove the applicability of the method in the context of realistic measurement configurations.

Medial Temporal Activation in Mal de Debarquement Syndrome Revealed by Standardized Low-Resolution Brain Electromagnetic Tomography

BackgroundMal de debarquement (MdD) literally means "sickness of disembarkation", and refers to the illusion of movement perceived as an after-effect of traveling on a boat, train, or airplane. The pathophysiology of MdD is currently unknown.Case ReportA 20-year-old man presented with dizziness and swaying sensation for 3 days after a boat trip. Compared with the follow-up EEG without symptoms, the EEG recorded while having MdD symptoms disclosed a significantly decreased alpha-band current source density at the precentral gyrus of the left frontal lobe and increased beta-2 activity at the parahippocampal gyrus of right mesial temporal region.ConclusionsOur results provide evidence of deranged cortical activity in MdD. To the best of our knowledge this is the first study to document cortical correlates of MdD using an EEG source-localization method.

Discriminant analysis and equivalent source localization of the EEG related to cognitive functions.

Discriminant analysis and EEG source localization methods were employed to compare groups of normal subjects during different cognitive conditions using 43-channel EEG recordings in the alpha (8-13 Hz) frequency band. Recordings were obtained from 69 dextral females during 2 passive conditions, Eyes-Open and Eyes-Closed, and 2 active conditions, Word-Finding and Dot-Localization. The cross-spectral matrix between all of the electrode sites was used to characterize the EEGs obtained during each condition. The subjects were partitioned into training and test sets and quadratic discriminant functions were constructed from the training sets to classify the EEGs. The discriminant functions successfully classified both the training and test sets at rates approaching 80%. The classification was repeated using only the diagonal (power spectral) elements of the cross-spectral matrices in the discriminant functions and this approach was successful in discriminating between the EEGs from the passive cognitive conditions but failed to discriminate between the EEGs from the active conditions. Source localization using a modified MUSIC algorithm indicated that the centers of brain electrical activity that distinguished the Eyes-Closed condition from the Eyes-Open condition were located in the medial occipital and right frontal regions. Centers of electrical activity that distinguished the Word-Finding condition from the Dot-Localization condition were located in the right medial posterior and left temporal regions. Validation of the locations of the centers of activity was accomplished by repeating the classification procedures using the spatial patterns generated on the scalp by dipole current sources placed at these locations.