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One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses. [ABSTRACT FROM AUTHOR]

Brain–computer interfaces based on electroencephalography (EEG) often exhibit unreliable performance in the classification of motor tasks. Recent research has shown that EEG source imaging (ESI) has the potential to outperform sensor domain approaches in various movement decoding tasks. However, ESI research to date has predominantly focused on the adult population, so its performance in youth with disabilities is unknown. In this study, we compared the offline classification performance of two ESI approaches (with and without modeling white matter conductivity anisotropy) to that of a sensor domain approach in the classification of left- versus right-hand movement execution and imagery tasks. Magnetic resonance images (MRI) were acquired from nine pediatric participants with brain lesions. Subsequently, cortical activity was recorded from 64 channels. MRI data were used to estimate participant-specific EEG sources. Various feature extraction and classification approaches were investigated in both sensor and source domains. Generally, ESI classification performance did not exceed chance levels and was statistically equivalent to sensor approaches except for isolated participants. However, ESI offered +9.61% improvement over the sensor domain (p = 0.031) in decoding motor execution in a participant with unilateral ventriculomegaly. Future research ought to delineate the specific task and participant characteristics which warrant the source domain approach. [ABSTRACT FROM AUTHOR]

Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research.

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized ${L}_{{2}{p}}$ -norm ( ${0} < {p} < {1}$ ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git.

Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance.

Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance. [ABSTRACT FROM AUTHOR]

Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized ${L}_{{2}{p}}$ -norm (${0} < {p} < {1}$) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance has not been paid sufficient attention. In this paper, a novel dipole feature imaging (DFI) and a hybrid convolutional neural network (HCNN) with an embedded squeeze-and-excitation block (SEB), denoted as DFI-HCNN, are proposed for decoding MI tasks. EEG source imaging technique is used for brain source estimation, and each sub-band spectrum powers of all dipoles are calculated through frequency analysis and band division. Then, the 3D space information of dipoles is retrieved, and by using azimuthal equidistant projection algorithm it is transformed to a 2D plane, which is combined with nearest neighbor interpolation to generate multi sub-band dipole feature images. Furthermore, a HCNN is designed and applied to the ensemble of sub-band dipole feature images, from which the importance of sub-bands is acquired to adjust the corresponding attentions adaptively by SEB. Ten-fold cross-validation experiments on two public datasets achieve the comparatively higher decoding accuracies of 84.23% and 92.62%, respectively. The experiment results show that DFI is an effective feature representation, and HCNN with an embedded SEB can enhance the useful frequency information of dipoles for improving MI decoding. [ABSTRACT FROM AUTHOR]

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The effective decoding of natural grasping behaviors is crucial for the natural control of neural prosthetics. This study aims to investigate the decoding performance of movement-related cortical potential (MRCP) source features between complex grasping actions and explore the temporal and frequency differences in inter-muscular and cortical-muscular coupling strength during movement. Based on the human grasping taxonomy and their frequency, five natural grasping motions—medium wrap, adducted thumb, adduction grip, tip pinch, and writing tripod—were chosen. We collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants, and projected six EEG frequency bands into source space for further analysis. Results from multi-classification and binary classification demonstrated that MRCP source features could not only distinguish between power grasp and precision grasp, but also detect subtle action differences such as thumb adduction and abduction during the execution phase. Besides, we found that during natural reach-and-grasp movement, the coupling strength from cortical to muscle is lower than that from muscle to cortical, except in the hold phase of $\gamma $ frequency band. Furthermore, a 12-Hz peak of inter-muscular coupling strength was found in movement execution, which might be related to movement planning and execution. We believe that this research will enhance our comprehension of the control and feedback mechanisms of human hand grasping and contributes to a natural and intuitive control for brain-computer interface.

The effective decoding of natural grasping behaviors is crucial for the natural control of neural prosthetics. This study aims to investigate the decoding performance of movement-related cortical potential (MRCP) source features between complex grasping actions and explore the temporal and frequency differences in inter-muscular and cortical-muscular coupling strength during movement. Based on the human grasping taxonomy and their frequency, five natural grasping motions—medium wrap, adducted thumb, adduction grip, tip pinch, and writing tripod—were chosen. We collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants, and projected six EEG frequency bands into source space for further analysis. Results from multi-classification and binary classification demonstrated that MRCP source features could not only distinguish between power grasp and precision grasp, but also detect subtle action differences such as thumb adduction and abduction during the execution phase. Besides, we found that during natural reach-and-grasp movement, the coupling strength from cortical to muscle is lower than that from muscle to cortical, except in the hold phase of $\gamma $ frequency band. Furthermore, a 12-Hz peak of inter-muscular coupling strength was found in movement execution, which might be related to movement planning and execution. We believe that this research will enhance our comprehension of the control and feedback mechanisms of human hand grasping and contributes to a natural and intuitive control for brain-computer interface. [ABSTRACT FROM AUTHOR]

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well. [ABSTRACT FROM AUTHOR]

Objective: Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole‐brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large‐scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls. Method: We defined neuronal avalanches as starting when the z‐scored source‐reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale‐free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients. Results: We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long‐term memory performance (delay recall Rey–Osterrieth Complex Figure Test). Significance: Our results show that the propagation patterns of large‐scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time–frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial–temporal features from multi sub-bands. [ABSTRACT FROM AUTHOR]

Aims: Drug-refractory epilepsy (DRE) refers to the failure of controlling seizures with adequate trials of two tolerated and appropriately chosen anti-seizure medications (ASMs). For patients with DRE, surgical intervention becomes the most effective and viable treatment, but its success rate is unsatisfactory at only approximately 50%. Predicting surgical outcomes in advance can provide additional guidance to clinicians. Despite the high accuracy of invasive methods, they inevitably carry the risk of post-operative infection and complications. Herein, to noninvasively predict surgical outcomes of DRE, we propose the "source causal connectivity" framework., Methods: In this framework, sLORETA, an EEG source imaging technique, was first used to inversely reconstruct intracranial neuronal electrical activity. Then, full convergent cross mapping (FCCM), a robust causal measure was introduced to calculate the causal connectivity between remodeled neuronal signals within epileptogenic zones (EZs). After that, statistical tests were performed to find out if there was a significant difference between the successful and failed surgical groups. Finally, a model for surgical outcome prediction was developed by combining causal network features with machine learning., Results: A total of 39 seizures with 205 ictal EEG segments were included in this prospective study. Experimental results exhibit that source causal connectivity in α-frequency band (8~13 Hz) gains the most significant differences between the surgical success and failure groups, with a p-value of 5.00e-05 and Cohen's d effect size of 0.68. All machine learning models can achieve an average accuracy of higher than 85%. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest accuracy of 90.73%, with a PPV of 87.91%, an NPV of 92.98%, a sensitivity of 90.91%, a specificity of 90.60%, and an F1-score of 89.39%., Conclusion: Our results demonstrate that the source causal network of EZ is a reliable biomarker for predicting DRE surgical outcomes. The findings promote noninvasive precision medicine for DRE., (© 2025 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

With continued advancement in computational technologies, the analysis of electroencephalography (EEG) has shifted from pure visual analysis to a noninvasive computational technique called EEG source imaging (ESI), which involves mathematical modeling of dipolar and distributed sources of a given scalp EEG pattern. ESI is a noninvasive phase I test for presurgical localization of the seizure onset zone in focal epilepsy. It is a relatively inexpensive modality, as it leverages scalp EEG and magnetic resonance imaging (MRI) data already collected typically during presurgical evaluation. With an adequate number of electrodes and combined with patient‐specific MRI‐based head models, ESI has proven to be a valuable and accurate clinical diagnostic tool for localizing the epileptogenic zone. Despite its advantages, however, ESI is routinely used at only a minority of epilepsy centers. This paper reviews the current evidence and practical fundamentals for using ESI of interictal and ictal epileptic activity during the presurgical evaluation of drug‐resistant patients. We identify common errors in processing and interpreting ESI studies, describe the differences in approach needed for localizing interictal and ictal EEG discharges through practical examples, and describe best practices for optimizing the diagnostic information available from these studies. [ABSTRACT FROM AUTHOR]

EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually “deep” in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging. [ABSTRACT FROM AUTHOR]

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface. [ABSTRACT FROM AUTHOR]

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.

Multimodal functional neuroimaging by integrating functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has the promise of recovering brain activities with high spatiotemporal resolution, which is crucial for neuroscience research and clinical diagnosis. However, the misalignment of the localizations between fMRI and EEG activities may degrade the accuracy of the fMRI-constrained EEG source imaging (ESI) technique. To leverage the complementary spatiotemporal resolution of fMRI and EEG in a data-driven fashion, we propose an asymmetric approach for EEG/fMRI fusion, termed fMRI-informed source imaging based on spatiotemporal basis functions (fMRI-SI-STBF). fMRI-SI-STBF employs the covariance components (CCs) derived from clusters defined by fMRI and EEG signals as spatial priors within the empirical Bayesian framework. Additionally, fMRI-SI-STBF represents the current source matrix as a linear combination of several unknown temporal basis functions (TBFs) by matrix decomposition. The relative contribution of each of the fMRI-informed and EEG-informed CCs, as well as the number and profiles of the TBFs, are all automatically determined based on the EEG data using variational Bayesian inference. Our results demonstrate that fMRI-SI-STBF can effectively utilize valid fMRI information for ESI and is robust to invalid fMRI priors. This robustness is essential for practical ESI since the validity of fMRI priors is often unclear considering that fMRI is an indirect measure of neural activity. Moreover, fMRI-SI-STBF can achieve performance improvement by incorporating temporal constraints compared to methods that use spatial constraints only. For the numerical simulations, fMRI-SI-STBF reconstructs the source extents, locations and time courses more accurately than existing EEG-fMRI ESI methods (i.e., fwMNE, fMRI-SI-SBF) and ESI methods without fMRI priors (i.e., wMNE, LORETA, SBL, SI-STBF, SI-SBF), indicated by the smaller spatial dispersion (average SD < 5 mm), distance of localization error (average DLE < 2 mm), shape error (average SE < 0.9) and larger model evidence values. [ABSTRACT FROM AUTHOR]

It is a long-standing challenge to reconstruct the locations and extents of cortical neural activities from electroencephalogram (EEG) recordings, especially when the EEG signals contain strong background activities and outlier artifacts. In this work, we propose a robust source imaging method called L 1 R-SSSI. To alleviate the effect of outliers in EEG, L 1 R-SSSI employs the L 1 -loss to model the residual error. To obtain locally smooth and globally sparse estimations, L 1 R-SSSI adopts the structured sparsity constraint, which incorporates the L 1 -norm regularization in both the variation and original source domain. The estimations of L 1 R-SSSI are efficiently obtained using the alternating direction method of multipliers (ADMM) algorithm. Results of simulated and experimental data analysis demonstrate that L 1 R-SSSI effectively suppresses the effect of the outlier artifacts in EEG. L 1 R-SSSI outperforms the traditional L 2 -norm-based methods (e.g., wMNE, LORETA), and SISSY, which employs L 2 -norm loss and structured sparsity, indicated by the larger AUC (average AUC > 0.80 ), smaller SD (average SD < 50 mm), DLE (average DLE < 10 mm) and RMSE (average RMSE < 1.75 ) values under all the numerically simulated conditions. L 1 R-SSSI also provides better estimations of extended sources than the method with L 1 -loss and L p -norm regularization term (e.g., LAPPS). [ABSTRACT FROM AUTHOR]

The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals. [ABSTRACT FROM AUTHOR]

Objective: Exploring the role of amygdala enlargement (AE) in temporal lobe epilepsy (TLE) without ipsilateral mesial temporal sclerosis (MTS) using comprehensive presurgical workup tools including traditional tools, automatically volumetric analysis, high-density EEG (HD-EEG) source imaging (HD-ESI), and stereoelectroencephalography (SEEG).Methods: Nine patients diagnosed with TLE-AE who underwent resective surgeries encompassing the amygdala were retrospectively studied. HD-ESI was obtained using 256-channel HD-EEG on the individualized head model. For automatic volumetric analysis, 48 matched controls were enrolled. Diagnosis and surgical strategies were based on a comprehensive workup following the anatomo-electro-clinical principle.Results: At post-operative follow-up (average 30.9 months), eight patients had achieved Engel class I and one Engel class II recovery. HD-ESI yielded unifocal source estimates in anterior mesial temporal region in 85.7% of cases. Automatic volumetric analysis showed the AE sides were consistent with the values determined through other preoperative workup tools. Furthermore, the amygdala volume of the affected sides in AE was significantly greater than that of the larger sides in controls (p < 0.001). Meanwhile, the amygdala volume lateral index (LI) of AE was significantly higher than in controls (p < 0.001). SEEG analysis showed that ictal onsets arose from the enlarged amygdala (and hippocampus) in all cases.Conclusion: In addition to traditional workup tools, automatic volumetric analysis, HD-ESI on individualized head model, and invasive SEEG can provide evidence of epileptogenicity in TLE-AE. Resective surgical strategies encompassing the amygdala result in better prognosis. In suspected TLE cases, more attention should be focused on detecting enlargement of amygdala which sometimes is "hidden" in "MR-negative" non-MTS cases. [ABSTRACT FROM AUTHOR]

• A novel subject-based dipole selection method named PRDS is proposed for decoding motor imagery tasks in the source space. • PRDS includes two steps: the preliminary selection of dipoles with the data-driven method and the refinement of dipoles based on continuous wavelet transform. • The dipoles selected by PRDS are fully activated and can best reflect the differences among the multiclass MI-tasks, meanwhile have good adaptability for different subjects. • The wavelet coefficient power of the selected dipoles are input to the OVO–CSP to extract the fusion features of time-frequency-spatial domains, thus yielding better decoding performance and calculation efficiency for the four classes of MI-tasks. In the BCI rehabilitation system, the decoding of motor imagery tasks (MI-tasks) with dipoles in the source domain has gradually become a new research focus. For complex multiclass MI-tasks, the number of activated dipoles is large, and the activation area, activation time and intensity are also different for different subjects. The means by which to identify fewer subject-based dipoles is very important. There exist two main methods of dipole selection: one method is based on the physiological functional partition theory, and the other method is based on human experience. However, the number of dipoles that are selected by the two methods is still large and contains information redundancy, and the selected dipoles are the same in both number and position for different subjects, which is not necessarily ideal for distinguishing different MI-tasks. In this paper, the data-driven method is used to preliminarily select fully activated dipoles with large amplitudes; the obtained dipoles are refined by using continuous wavelet transform (CWT) to best reflect the differences among the multiclass MI-tasks, thereby yielding a subject-based dipole selection method, which is named PRDS. PRDS is further used to decode multiclass MI-tasks in which some representative dipoles are found, and their wavelet coefficient power is calculated and input to one-vs.-one common spatial pattern (OVO-CSP) for feature extraction, and the features are classified by the support vector machine. We denote this decoding method as D-CWTCSP, which enhances the spatial resolution and also makes full use of the time-frequency-spatial domain information. Experiments are carried out using a public dataset with nine subjects and four classes of MI-tasks, and the proposed D-CWTCSP is compared with the relevant methods in sensor space and brain-source space in terms of the decoding accuracy, standard deviation, recall rate and kappa value. The experimental results show that D-CWTCSP reaches an average decoding accuracy of 82.66% among the nine subjects, which generates 8–20% improvement over other methods, thus reflecting its great superiority in decoding accuracy. [ABSTRACT FROM AUTHOR]

The neural generators of the frequency-following response (FFR), a neural response widely used to study the human auditory system, remain unclear. There is evidence that the balance between cortical and subcortical contributions to the FFR varies with stimulus frequency. In this study, we tried to clarify whether this variation extended to subcortical nuclei at higher stimulus frequencies where cortical sources were inactive. We evoked FFRs, in 17 human listeners with normal hearing (9 female), with three complex tones with missing-fundamentals corresponding to musical tones C4 (262 Hz), E4 (330 Hz), and G4 (393 Hz) presented to left, right, or both ears. Source imaging results confirmed the dominance of subcortical activity underlying both fundamental frequency (F0) and second harmonic (H2) components of the FFR. Importantly, several FFR features (spatial complexity, scalp distributions of spectral strength and inter-trial phase coherence, and functional connectivity patterns) varied systematically with stimulus F0, suggesting an unfixed source configuration. We speculated that the variation of FFR source configuration with stimulus frequency resulted from changing relative contributions of subcortical nuclei. Supportively, topographic comparison between the FFR and the auditory brainstem response (ABR) evoked by clicks revealed that the topography of the F0 component resembled that of the click-ABR at an earlier latency when stimulus F0 was higher and that the topography of the H2 component resembled that of the click-ABR at a nearly fixed latency regardless of stimulus F0, particularly for binaurally evoked FFRs. Possible generation sites of the FFR and implications for future studies were discussed.

The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations. With the half and fifth sensitivity volumes we determine both the theoretical minimum electrode separation for non-redundant scalp EEG measurements and the electrode sensitivity resolution, which vary over the scalp because of the head geometry. According to our results, electrodes should be at least ~3 to 3.5 mm apart for an optimal configuration. The sensitivity resolution is generally worse for electrodes at the boundaries of the scalp measured region, though, by analogy with human montages, concentrates the sensitivity enough to localize sources. Cramér–Rao lower bounds of source localization errors indicate it is theoretically possible to achieve ESI accuracy at the level of anatomical structures, such as the stimulus-specific somatosensory areas, using the template. More validation for this approximation is provided through the comparison between the template and the individual lead field matrices, for several rats. Finally, using well-accepted inverse methods, we demonstrate that somatosensory ESI is not only expected but also allows exploring unknown phenomena related to global sensory integration. Inheriting the advantages and pitfalls of human ESI, rat ESI will boost the understanding of brain pathophysiological mechanisms and the evaluation of ESI methodologies, new pharmacological treatments and ESI-based biomarkers. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic brain functional network during anesthesia is supported by 5 metastable network patterns. • The occurrence of each metastable network pattern is associated with the level of anesthesia. • An anatomical connectivity supported functional network pattern persists across conscious levels. Abstract Objective We investigated the changes of dynamic brain functional network from awaken state to the anesthesia level suitable for surgery. Methods 60-channel EEG data of 22 subjects are acquired at wakefulness, light anesthesia and deep anesthesia. The activity of 68 cortical regions are obtained by using EEG source imaging. Sliding window analysis is employed to obtain a dynamic sequence of brain functional network. K-means clustering algorithm is then employed to identify the common brain functional network patterns. Results Five common brain functional network patterns were identified across all conscious levels. The occurrence of each meta-stable network pattern was associated with the level of anesthesia. A transition functional network pattern was found to transfer to the anesthesia dominating or wakefulness dominating network pattern depending on the conscious level. Furthermore, a functional network pattern persisted during both wakefulness and anesthesia is found to be supported by the anatomical connectivity. Conclusions Dynamic changes of brain functional network exist in both awaken and anesthesia state. Significance These findings suggest that dynamic brain functional network analysis plays a critical role in decoding the mechanism of general anesthesia. The obtained five metastable network patterns may be employed for monitoring the depth of anesthesia. [ABSTRACT FROM AUTHOR]

The neural generators of the frequency-following response (FFR), a neural response widely used to study the human auditory system, remain unclear. There is evidence that the balance between cortical and subcortical contributions to the FFR varies with stimulus frequency. In this study, we tried to clarify whether this variation extended to subcortical nuclei at higher stimulus frequencies where cortical sources were inactive. We evoked FFRs, in 17 human listeners with normal hearing (9 female), with three complex tones with missing-fundamentals corresponding to musical tones C4 (262 Hz), E4 (330 Hz), and G4 (393 Hz) presented to left, right, or both ears. Source imaging results confirmed the dominance of subcortical activity underlying both fundamental frequency (F0) and second harmonic (H2) components of the FFR. Importantly, several FFR features (spatial complexity, scalp distributions of spectral strength and inter-trial phase coherence, and functional connectivity patterns) varied systematically with stimulus F0, suggesting an unfixed source configuration. We speculated that the variation of FFR source configuration with stimulus frequency resulted from changing relative contributions of subcortical nuclei. Supportively, topographic comparison between the FFR and the auditory brainstem response (ABR) evoked by clicks revealed that the topography of the F0 component resembled that of the click-ABR at an earlier latency when stimulus F0 was higher and that the topography of the H2 component resembled that of the click-ABR at a nearly fixed latency regardless of stimulus F0, particularly for binaurally evoked FFRs. Possible generation sites of the FFR and implications for future studies were discussed. [ABSTRACT FROM AUTHOR]

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results. [ABSTRACT FROM AUTHOR]

Sustained attention is crucial for higher-order cognition and real-world activities. The idea that tactile training improves sustained attention is appealing and has clinical significance. The aim of this study was to explore whether tactile training could improve visual sustained attention. Using 128-channel electroencephalography (EEG), we found that participants with tactile training outperformed non-trainees in the accuracy and calculation efficiency measured by the Math task. Furthermore, trainees demonstrated significantly decreased omission error measured by the sustained attention to response task (SART). We also found that the improvements in behavioral performance were associated with parietal P300 amplitude enhancements. EEG source imaging analyses revealed stronger brain activation among the trainees in the prefrontal and sensorimotor regions at P300. These results suggest that the tactile training can improve sustained attention in young adults, and the improved sustained attention following training may be due to more effective attentional resources allocation. Our findings also indicate the use of a noninvasive tactile training paradigm to improve cognitive functions (e.g., sustained attention) in young adults, potentially leading to new training and rehabilitative protocols.

Abstract: Visual selective attention operates through top–down mechanisms of signal enhancement and suppression, mediated by α‐band oscillations. The effects of such top–down signals on local processing in primary visual cortex (V1) remain poorly understood. In this work, we characterize the interplay between large‐scale interactions and local activity changes in V1 that orchestrates selective attention, using Granger‐causality and phase‐amplitude coupling (PAC) analysis of EEG source signals. The task required participants to either attend to or ignore oriented gratings. Results from time‐varying, directed connectivity analysis revealed frequency‐specific effects of attentional selection: bottom–up γ‐band influences from visual areas increased rapidly in response to attended stimuli while distributed top–down α‐band influences originated from parietal cortex in response to ignored stimuli. Importantly, the results revealed a critical interplay between top–down parietal signals and α–γ PAC in visual areas. Parietal α‐band influences disrupted the α–γ coupling in visual cortex, which in turn reduced the amount of γ‐band outflow from visual areas. Our results are a first demonstration of how directed interactions affect cross‐frequency coupling in downstream areas depending on task demands. These findings suggest that parietal cortex realizes selective attention by disrupting cross‐frequency coupling at target regions, which prevents them from propagating task‐irrelevant information. [ABSTRACT FROM AUTHOR]

We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm. In the first stage, we optimize a simplified non-sparse generative model to get an estimate of the sensor noise covariance and a good initialization of the group-sparsity profile of the sources. Sources obtained at this stage are equivalent to those estimated with the popular inverse method LORETA. In the second stage, we apply a fast SBL algorithm with the noise covariance fixed to the value obtained in the first stage to efficiently shrink to zero groups of sources that are irrelevant for explaining the EEG measurements. In addition, we derive an initialization to the first stage of the algorithm that is optimal in the least squares sense, which prevents delays due to suboptimal initial conditions. We validate our method on both simulated and real EEG data. Simulations show that the method is robust to measurement noise and performs well in real-time, with faster performance than two state of the art SBL solvers. On real error-related negativity EEG data, we obtain source images in agreement with the experimental literature. The method shows promise for real-time neuroimaging and brain-machine interface applications. [ABSTRACT FROM AUTHOR]

Objective: Dipole fit source (DFS) localisation is a non-invasive imaging process used to identify the epileptogenic zone (EZ) in the brain. The purpose of the present study was to verify the use of DFS localisation for identifying the EZ in patients with and without lesions using magnetic resonance imaging (MRI). Methods: In this study, DFS localisation was used in 16 patients, of whom 7 had no lesions and 9 had lesions on MRI post-surgery, with at least 3 years of follow-up data. For DFS localisation, different scalp electroencephalogram (EEG) ictal activity was assessed (ictal spikes, rhythmic, paroxysmal fast, and obscured activity). DFSs were superimposed with postoperative MRIs to confirm the accuracy of the determined EZs. Results: The DFS correctly identified EZ localization within the resection area in 14 of the 16 patients. These 14 patients were all seizure free after surgery. The two remaining patients, in whom the DFS was adjacent to the resected area, had a decreased seizure frequency following surgery. Conclusions: DFSs determined during preoperative evaluations can provide information on EZ lateralisation and localisation and contribute to the presurgical decision process. Thus, the accurate identification of EZ boundaries is important and can be achieved more reliably with the use of multiple quantitative EEG analysis methods. [ABSTRACT FROM AUTHOR]

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.

Humans are sensitive to gaze direction from early life, and gaze has social and affective values. Borderline personality disorder (BPD) is a clinical condition characterized by emotional dysregulation and enhanced sensitivity to affective and social cues. In this study we wanted to investigate the temporal-spatial dynamics of spontaneous gaze processing in BPD. We used a 2-back-working-memory task, in which neutral faces with direct and averted gaze were presented. Gaze was used as an emotional modulator of event-related-potentials to faces. High density EEG data were acquired in 19 females with BPD and 19 healthy women, and analyzed with a spatio-temporal microstates analysis approach. Independently of gaze direction, BPD patients showed altered N170 and P200 topographies for neutral faces. Source localization revealed that the anterior cingulate and other prefrontal regions were abnormally activated during the N170 component related to face encoding, while middle temporal deactivations were observed during the P200 component. Post-task affective ratings showed that BPD patients had difficulty to disambiguate neutral gaze. This study provides first evidence for an early neural bias toward neutral faces in BPD independent of gaze direction and also suggests the importance of considering basic aspects of social cognition in identifying biological risk factors of BPD. [ABSTRACT FROM AUTHOR]

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setupsmore accurately and with amore robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website. [ABSTRACT FROM AUTHOR]

EEG source imaging enables us to reconstruct current density in the brain from the electrical measurements with excellent temporal resolution (~ms). The corresponding EEG inverse problem is an ill-posed one that has infinitely many solutions. This is due to the fact that the number of EEG sensors is usually much smaller than that of the potential dipole locations, as well as noise contamination in the recorded signals. To obtain a unique solution, regularizations can be incorporated to impose additional constraints on the solution. An appropriate choice of regularization is critically important for the reconstruction accuracy of the brain image. In this paper, we propose a novel Sparsity and SMOOthness enhanced brain TomograpHy (s-SMOOTH) method to improve the reconstruction accuracy by integrating two recently proposed regularization techniques: Total Generalized Variation (TGV) regularization and l_(1-2) regularization. TGV is able to preserve the source edge and recover the spatial distribution of the source intensity with high accuracy. Compared to the relevant total variation (TV) regularization, TGV enhances the smoothness of the image and reduces staircasing artifacts. The traditional TGV defined on a 2D image has been widely used in image processing field. In order to handle 3D EEG source images, we propose a voxel-based TGV (vTGV) regularization that extends the definition of second-order TGV from 2D planar image to 3D irregular surfaces such as cortex surface. In addition, the l_(1-2) regularization is utilized to promote sparsity on the current density itself. We demonstrate that l_(1-2) regularization is able to enhance sparsity and accelerate computations than l_1 regularization. The proposed model is solved by an efficient and robust algorithm based on the difference of convex functions algorithm (DCA) and the alternating direction method of multipliers (ADMM). Numerical experiments using synthetic data demonstrate the advantages of the proposed method over other state-of-the-art methods in terms of total reconstruction accuracy, localization accuracy and focalization degree. The application to the source localization of event-related potential data further demonstrates the performance of the proposed method in real-world scenario.

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, such as EEG-correlated fMRI, temporally coherent networks and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach towards the study of brain networks in cognitive and clinical neurosciences.