The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.

Motor imagery-based brain-computer interface (MI-BCI) faces a critical challenge in achieving effective spatial-temporal feature modeling while maintaining a compact model parameterization. Herein, a lightweight model was proposed, termed as Dual-Attention-EEGNet (DA-EEGNet), which extends the EEGNet backbone by integrating a channel attention module and a depth attention module to selectively emphasize informative electrodes and temporally discriminative features. Two widely used MI benchmark datasets and three evaluation strategies, i.e. subject-dependent scenario, subject-independent scenario, and dataset-independent classification scenario, were utilized to verify the model's performance. Despite its compact design, DA-EEGNet contains merely 3.97[Formula: see text]k trainable parameters and achieves average classification accuracies of [Formula: see text] and [Formula: see text], outperforming or matching existing deep learning approaches that rely on substantially larger parameter counts. Ablation studies further confirm the complementary contributions of the channel and depth attention modules. In addition, visualization analyses, including temporal attention heatmaps and motor-area topographies, demonstrate that DA-EEGNet captures neurophysiologically meaningful spatial-temporal patterns consistent with MI-related brain activity. These results indicate that DA-EEGNet provides a favorable parameter-accuracy trade-off and serves as an efficient and interpretable baseline for MI-BCI applications.

Effective regulation of social decision-making is crucial for achieving equitable outcomes in human interactions. This study explores the impact of endogenous regulation on social decision-making and associated neural changes through a neurofeedback (NF) training framework. Given the relationship between social decision making, emotions, and frontal alpha asymmetry (FAA), this NF training enables individuals to self-regulate their FAA, thereby influencing their decision-making behavior. Eighty-one participants were randomly divided into the up-FAA group aiming at up-regulating FAA, the down-FAA group aiming at down-regulating FAA, and the sham-NF group. First, our results validated the specific NF training effect on selfregulating FAA. Notably, not all participants in the up-FAA and down-FAA groups successfully learned to regulate their FAA, leading to further subdivision into up-learner, down-learner, up-nonlearner, and down-nonlearner categories based on learning efficacy. Participants who effectively learned to reduce their FAA (down-learners) showed significant changes in decision behavior under moderately unfair conditions, characterized by increased rejection rates during the ultimatum game (UG) task. They also exhibited larger N200 amplitudes while balancing the decisionmaking period. In contrast, up learners demonstrated minimal behavioral changes despite increases in FAA. We conclude that decreases in FAA have a more pronounced impact on social decision-making than increases during NF training. This study highlights the effects of FAA self-regulation on fairness-related decision-making, revealing the neurobiological factors that shape decisions influenced by fairness perceptions. These findings offer valuable insights for enhancing social cooperation and justice.

The interaction of the brain's decision-making and feedback stages is crucial for guiding human behavior. Previous studies mainly focused on the interaction immediately after the feedback, resulting in a limited understanding of brain communication dynamics during the interaction process. This study examined the communication dynamics of the brain network during decision-feedback interaction under various feedback conditions by employing a newly developed activation network approach to reveal its underlying neural mechanism. Thirty participants completed a decision-feedback task that involved a sequence of cue-induced predictions with highly predictable, somewhat predictable, and unpredictable feedback conditions. We constructed the activation network for all experimental stages using source-level EEG data in the alpha band. Notably, the brain exhibited the highest communication efficiency ($p < 0.05$) in receiving and integrating feedback with decision-making information during the feedback stage. Furthermore, the network-behavior correlations indicated that the brain tends to evaluate unexpected feedback under highly predictable conditions and expected feedback under unpredictable conditions, suggesting distinct neural strategies of the decision-feedback interaction process. Finally, we decoded the optimization process of decision-feedback interaction across the entire task. Although network correlations between the decision and feedback stages decreased over time (high predictable: $r = -0.447$, $p = 0.001$; unpredictable: $r = -0.305$, $p = 0.032$), classification accuracy significantly improved (${r = -0.448}$, $p = 0.010$, best accuracy: 86.667% ) under the highly predictable condition, corresponding with enhanced prediction behavior. These results indicate the optimization process of the cognitive resources allocation that supports more efficient interaction and improved predictive performance. Our findings advance the understanding of the mechanisms of decision-feedback interaction.

Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and stable models. However, due to the significant heterogeneity and domain shift in rs-fMRI data across different sites, the accuracy of auxiliary diagnosis remains unsatisfactory. Moreover, there has been limited exploration of multi-source domain adaptation models on ASD recognition, and many existing models lack inherent interpretability, as they do not explicitly incorporate prior neurobiological knowledge such as the hierarchical structure of functional brain networks. To address these challenges, we proposed a domain-adaptive algorithm based on hyperbolic space embedding. Hyperbolic space is naturally suited for representing the topology of complex networks such as brain functional networks. Therefore, we embedded the brain functional network into hyperbolic space and constructed the corresponding hyperbolic space community network to effectively extract latent representations. To address the heterogeneity of data across different sites and the issue of domain shift, we introduce a constraint loss function, Hyperbolic Maximum Mean Discrepancy (HMMD), to align the marginal distributions in the hyperbolic space. Additionally, we employ class prototype alignment to mitigate discrepancies in conditional distributions across domains. Experimental results indicate that the proposed algorithm achieves superior classification performance for ASD compared to baseline models, with improved robustness to multi-site heterogeneity. Specifically, our method achieves an average accuracy improvement of 4.03% . Moreover, its generalization capability is further validated through experiments conducted on extra Major Depressive Disorder (MDD) datasets.

Emotion recognition based on brain networks has attracted increasing research attention due to its ability to reveal the information interactions between brain regions under different emotional states. However, there are still two challenges in practical applications: 1) The high dimensionality of brain networks can lead to issues of feature redundancy, overfitting, and high computational costs; 2) Electroencephalography (EEG) signals are susceptible to outlier noise, and label noise caused by mismatches between the stimuli labels used to induce emotions and the individuals’ actual emotional responses can significantly impact emotion recognition. To address these challenges, we propose a supervised graph embedding algorithm based on the L1-norm space (L1-SGE). This method leverages the local structure and class information of the original data for discriminative subspace learning, achieving a low-dimensional representation of high-dimensional networks. Additionally, the constraints of the L1-norm space enable the method to effectively suppress outliers and label noise. The performance on publicly available emotional EEG databases has successfully validated the effectiveness of the proposed method in low-dimensional feature representation and noise suppression. Furthermore, this method not only offers a powerful tool for research in affective brain-computer interfaces but also provides a potential solution for pattern recognition tasks facing similar challenges in the field of artificial intelligence.

Real-time emotion recognition provides promising applications for mental healthcare monitoring and human-computer interaction design. Electroencephalography (EEG) emotion recognition has become a hot topic in the field of affective computing and intelligent brain-computer interface (BCI), and it is a feasible solution for achieving real-time emotion recognition. However, due to the uncertainty and individual specificity of emotional cognition, there are still some challenges in achieving efficient online emotion decoding applications. To address this, in this work, we propose an online emotion decoding method named DMSGL (Real-Time EEG Emotion Recognition from Dynamic Mixed Spatiotemporal Graph Learning). Specifically, in the DMSGL, we propose to explore the latent emotion-related graph features from EEG with cognition-inspired and data-driven learning strategies, and the temporal analysis with attention learning is utilized to further extract the robust spatiotemporal graph patterns for efficient EEG emotion decoding. Both simulated online emotion decoding and real-time emotion monitoring experimental results have consistently indicated that the proposed DMSGL can effectively satisfy the application requirements of real-time emotion decoding and achieves an accuracy of 68.35% in real-world online scenarios. Compared with other baseline methods, the proposed DMSGL has improved by 2-5% in the scenario of real-time emotion recognition. In conclusion, the proposed DMSGL provides a promising solution for realizing real-time emotion recognition and further exploring related applications. Our code is released on https://github.com/UESTC-BAC/DMSGL.

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems.

Electroencephalography (EEG) is an objective tool for emotion recognition with promising applications. However, the scarcity of labeled data remains a major challenge in this field, limiting the widespread use of EEG-based emotion recognition. In this paper, a semi-supervisedDual-streamSelf-attentiveAdversarialGraphContrastive learning framework (termed asDS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition. The DS-AGC framework includes two parallel streams for extracting non-structural and structural EEG features. The non-structural stream incorporates a semi-supervised multi-domain adaptation method to alleviate distribution discrepancy among labeled source domain, unlabeled source domain, and unknown target domain. The structural stream develops a graph contrastive learning method to extract effective graph-based feature representation from multiple EEG channels in a semi-supervised manner. Further, a self-attentive fusion module is developed for feature fusion, sample selection, and emotion recognition, which highlights EEG features more relevant to emotions and data samples in the labeled source domain that are closer to the target domain. Extensive experiments are conducted on four benchmark databases (SEED, SEED-IV, SEED-V, and FACED) using a semi-supervised cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show that the proposed model outperforms existing methods under different incomplete label conditions with an average improvement of 2.17%, which demonstrates its effectiveness in addressing the label scarcity problem in cross-subject EEG-based emotion recognition.

Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ( $p \lt 0.001$ ) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system [Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN)] inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.

Cognitive processes induced by the specific task are underpinned by intrinsic anatomical structures with functional neural activation patterns. However, current covariance network analysis still pays much attention to brain morphologies or baseline activity due to the lack of an effective method for capturing the structural-functional covarying during tasks. Here, a multimodal covariance network (MCN) construction method was proposed to identify inter-regional covariations of the structural skeleton and functional activities by simultaneous magnetic resonance imaging and electroencephalogram (EEG). Results from two independent cohorts confirmed that MCNs could capture cognition-specific hierarchical modules in joint comprehensive multimodal features well, especially when time-resolved EEG was further integrated. The quantitative evaluation further demonstrates significantly larger modularity of MCN integrating fine-grained features from EEG. The application to the discovery cohort identified prominent modular covarying across the default mode and salience networks at rest, while the visual oddball task was accomplished by synchronous structural-functional cooperation within networks associated with attention control and working memory updating. Strikingly, the results of an external validation cohort showed a different covariant pattern corresponding to decision-specific cognitive modules. Overall, the results suggested that multimodal covariance analysis provides a reliable definition of multistate neural cognitive networks, further discloses modular-specific structural and functional co-variation.

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing "Primary peak" and "P3-like peak" in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

As a novel approach for people to directly communicate with an external device, the study of brain-computer interfaces (BCIs) has become well-rounded. However, similar to the real-world scenario, where individuals are expected to work in groups, the BCI systems should be able to replicate group attributes. We proposed a 4-order cumulants feature extraction method (CUM4-CSP) based on the common spatial patterns (CSP) algorithm. Simulation experiments conducted using motion visual evoked potentials (mVEP) EEG data verified the robustness of the proposed algorithm. In addition, to freely choose paradigms, we adopted the mVEP and steady-state visual evoked potential (SSVEP) paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP algorithm. The feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external devices. To verify the robustness of the proposed scheme, we recruited 30 subjects to conduct online game control experiments, and the results were statistically analyzed. The simulation results prove that the proposed CUM4-CSP algorithm has good noise immunity. The online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI paradigm. The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy environment. Additionally, the proposed scheme may provide a new solution for EEG-based group BCI research.

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment.Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications.EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas.In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years.Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals.Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications.Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.!

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.

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

The basal ganglia (BG), serving as an intermediate bridge between the cerebral cortex and thalamus, are believed to play crucial roles in controlling absence seizure activities generated by the pathological corticothalamic system. Inspired by recent experiments, here we systematically investigate the contribution of a novel identified GABAergic pallido-cortical pathway, projecting from the globus pallidus externa (GPe) in the BG to the cerebral cortex, to the control of absence seizures. By computational modelling, we find that both increasing the activation of GPe neurons and enhancing the coupling strength of the inhibitory pallido-cortical pathway can suppress the bilaterally synchronous 2-4 Hz spike and wave discharges (SWDs) during absence seizures. Appropriate tuning of several GPe-related pathways may also trigger the SWD suppression, through modulating the activation level of GPe neurons. Furthermore, we show that the previously discovered bidirectional control of absence seizures due to the competition between other two BG output pathways also exists in our established model. Importantly, such bidirectional control is shaped by the coupling strength of this direct GABAergic pallido-cortical pathway. Our work suggests that the novel identified pallido-cortical pathway has a functional role in controlling absence seizures and the presented results might provide testable hypotheses for future experimental studies.

Absence epilepsy is believed to be associated with the abnormal interactions between the cerebral cortex and thalamus. Besides the direct coupling, anatomical evidence indicates that the cerebral cortex and thalamus also communicate indirectly through an important intermediate bridge-basal ganglia. It has been thus postulated that the basal ganglia might play key roles in the modulation of absence seizures, but the relevant biophysical mechanisms are still not completely established. Using a biophysically based model, we demonstrate here that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. Under certain conditions, these two types of seizure control are observed to coexist in the same network. More importantly, due to the competition between the inhibitory SNr-TRN and SNr-SRN pathways, we find that both decreasing and increasing the activation of SNr neurons from the normal level may considerably suppress the generation of spike-and-slow wave discharges in the coexistence region. Overall, these results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder.

The only established technique for intracranial pressure (ICP) measurement is an invasive procedure requiring surgically penetrating the skull for placing pressure sensors. However, there are many clinical scenarios where a noninvasive assessment of ICP is highly desirable. With an assumption of a linear relationship among arterial blood pressure (ABP), ICP, and flow velocity (FV) of major cerebral arteries, an approach has been previously developed to estimate ICP noninvasively, the core of which is the linear estimation of the coefficients f between ABP and ICP from the coefficients w calculated between ABP and FV. In this paper, motivated by the fact that the relationships among these three signals are so complex that simple linear models may be not adequate to depict the relationship between these two coefficients, i.e., f and w , we investigate the adoption of several nonlinear kernel regression approaches, including kernel spectral regression (KSR) and support vector machine (SVM) to improve the original linear ICP estimation approach. The ICP estimation results on a dataset consisting of 446 entries from 23 patients show that the mean ICP error by the nonlinear approaches can be reduced to below 6.0 mmHg compared to 6.7 mmHg of the original approach. The statistical test also demonstrates that the ICP error by the proposed nonlinear kernel approaches is statistically smaller than that estimated with the original linear model (p < 0.05). The current result confirms the potential of using nonlinear regression to achieve more accurate noninvasive ICP assessment.