Nonparametric Dynamic Granger Causality based on Multi-Space Spectrum Fusion for Time-varying Directed Brain Network Construction.

Nonparametric estimation of time-varying directed networks can unveil the intricate transient organization of directed brain communication while circumventing constraints imposed by prescribed model-driven methods. A robust time-frequency representation - the foundation of its causality inference - is critical for enhancing its reliability. This study proposed a novel method, i.e., nonparametric dynamic Granger causality based on Multi-space Spectrum Fusion (ndGCMSF), which integrates complementary spectrum information from different spaces to generate reliable spectral representations to estimate dynamic causalities across brain regions. Systematic simulations and validations demonstrate that ndGCMSF exhibits superior noise resistance and a powerful ability to capture subtle dynamic changes in directed brain networks. Particularly, ndGCMSF revealed that during instruction response movements, the laterality in the hemisphere ipsilateral to the hemiplegic limb emerges upon instruction onset and diminishes upon task accomplishment. These intrinsic variations further provide reliable features for distinguishing two types of hemiplegia (left vs. right) and assessing motor functions. The ndGCMSF offers powerful functional patterns to derive effective brain networks in dynamically changing operational settings and contributes to extensive areas involving dynamical and directed communications.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories.

BACKGROUND: Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. METHODS: Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. RESULTS: EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. CONCLUSION: The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Transforming of scalp EEGs with different channel locations by REST for comparative study.

OBJECTIVE: The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline. METHODS: Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations. RESULTS: REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one. CONCLUSION: These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.

Prediction of rTMS Efficacy in Patients With Essential Tremor: Biomarkers From Individual Resting-State EEG Network.

The pathogenesis of essential tremor (ET) remains unclear, and the efficacy of related drug treatment is inadequate for proper tremor control. Hence, in the current study, consecutive low-frequency repetitive transcranial magnetic stimulation (rTMS) modulation on cerebellum was accomplished in a population of ET patients, along with pre- and post-treatment resting-state electroencephalogram (EEG) networks being constructed. The results primarily clarified the decreasing of resting-state network interactions occurring in ET, especially the weaker frontal-parietal connectivity, compared to healthy individuals. While after the rTMS stimulation, promotions in both network connectivity and properties, as well as clinical scales, were identified. Furthermore, significant correlations between network characteristics and clinical scale scores enabled the development of predictive models for assessing rTMS intervention efficacy. Using a multivariable linear model, clinical scales after one-month rTMS treatment were accurately predicted, underscoring the potential of brain networks in evaluating rTMS effectiveness for ET. The findings consistently demonstrated that repetitive low-frequency rTMS neuromodulation on cerebellum can significantly improve the manifestations of ET, and individual networks will be reliable tools for evaluating the rTMS efficacy, thereby guiding personalized treatment strategies for ET patients.

Unveiling perinatal depression: A dual-network EEG analysis for diagnosis and severity assessment.

Perinatal depression (PD), which affects about 10-20 percent of women, often goes unnoticed because related symptoms frequently overlap with those commonly experienced during pregnancy. Moreover, identifying PD currently depends heavily on the use of questionnaires, and objective biological indicators for diagnosis has yet to be identified. This research proposes a safe and non-invasive method for diagnosing PD and aims to delve deeper into its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram (EEG) for mothers-to-be and fetuses, we collected the resting-state scalp EEG of pregnant women (with PD/healthy) at the 38th week of gestation. To compensate for the low spatial resolution of scalp EEG, source analysis was first applied to project the scalp EEG to the cortical-space. Afterwards, cortical-space networks and large-scale networks were constructed to investigate the mechanism of PD from two different level. Herein, differences in the two distinct types of networks between PD patients and healthy mothers-to-be were explored, respectively. We found that the PD patients illustrated decreased network connectivity in the cortical-space, while the large-scale networks revealed weaker connections at cerebellar area. Further, related spatial topological features derived from the two different networks were combined to promote the recognition of pregnant women with PD from those healthy ones. Meanwhile, the depression severity at patient level was effectively predicted based on the combined spatial topological features as well. These findings consistently validated that the two kinds of networks indeed played off each other, which thus helped explore the underlying mechanism of PD; and further verified the superiority of the combination strategy, revealing its reliability and potential in diagnosis and depression severity evaluation.

Hydrogen sulfide antagonizes cytokinin to change root system architecture through persulfidation of CKX2 in Arabidopsis.

Hydrogen sulfide (H2S) is an endogenous gaseous signaling molecule, which has been shown to play an important role in plant growth and development by coupling with various phytohormones. However, the relationship between H2S and cytokinin (CTK) and the mechanisms by which H2S and CTK affect root growth remain poorly understood. Endogenous CTK was analyzed by UHPLC-ESI-MS/MS. Persulfidation of cytokinin oxidase/dehydrogenases (CKXs) was analyzed by mass spectrometry (MS). ckx2/CKX2wild-type (WT), OE CKX2 and ckx2/CKX2Cys(C)62alanine(A) transgenic lines were isolated with the ckx2 background. H2S is linked to CTK content by CKX2, which regulates root system architecture (RSA). Persulfidation at cysteine (Cys)62 residue of CKX2 enhances CKX2 activity, resulting in reduced CTK content. We utilized 35S-LCD/oasa1 transgenic lines to investigate the effect of endogenous H2S on RSA, indicating that H2S reduces the gravitropic set-point angle (GSA), shortens root hairs, and increases the number of lateral roots (LRs). The persulfidation of CKX2Cys62 changes the elongation of cells on the upper and lower flanks of LR elongation zone, confirming that Cys62 of CKX2 is the specificity target of H2S to regulate RSA in vivo. In conclusion, this study demonstrated that H2S negatively regulates CTK content and affects RSA by persulfidation of CKX2Cys62 in Arabidopsis thaliana.

Sleep stages classification by fusing the time-related synchronization analysis and brain activations.

Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

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.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology.

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the ß (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the ß and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia.

Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.

An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. This model introduces a multi-branch structure for sub-bands and artificially configures attention focus factors on various branches, resulting in distinct attention distributions for different sub-bands. Experimental results demonstrate that when DSFMANet processes sub-band data, its performance surpasses current benchmarks in key metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). This success is particularly evident in terms of MSE and MAE, where the performance of sub-band data is significantly superior, highlighting the model's potential in accurately predicting HAMD-17 scores. Concurrently, the experiment also compared the model's performance with sub-band and full-band data, affirming the superiority of the selective focus attention mechanism in electroencephalography (EEG) signal processing. DSFMANet, when utilizing sub-band data, exhibits higher data processing efficiency and reduced model complexity. The findings of this study underscore the significance of employing deep learning models based on sub-band analysis in depression diagnosis. The DSFMANet model not only effectively enhances the accuracy of depression diagnosis but also offers valuable research directions for similar applications in the future. This deep learning-based automated approach can effectively ascertain the HAMD-17 scores of patients with depression, thus offering more accurate and reliable support for clinical decision-making.

Evaluating the depression state during perinatal period by non-invasive scalp EEG.

Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.

Multimodal Covariance Network Reflects Individual Cognitive Flexibility.

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.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit.

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Size Dependent Phase Transformation of Liquid Gallium.

As the most popular liquid metal (LM), gallium (Ga) and its alloys are emerging as functional materials due to their unique combination of fluidic and metallic properties near room temperature. As an important branch of utilizing LMs, micro- and submicron-particles of Ga-based LM are widely employed in wearable electronics, catalysis, energy, and biomedicine. Meanwhile, the phase transition is crucial not only for the applications based on this reversible transformation process, but also for the solidification temperature at which fluid properties are lost. While Ga has several solid phases and exhibits unusual size-dependent phase behavior. This complex process makes the phase transition and undercooling of Ga uncontrollable, which considerably affects the application performance. In this work, extensive (nano-)calorimetry experiments are performed to investigate the polymorph selection mechanism during liquid Ga crystallization. It is surprisingly found that the crystallization temperature and crystallization pathway to either α -Ga or ß -Ga can be effectively engineered by thermal treatment and droplet size. The polymorph selection process is suggested to be highly relevant to the capability of forming covalent bonds in the equilibrium supercooled liquid. The observation of two different crystallization pathways depending on the annealing temperature may indicate that there exist two different liquid phases in Ga.

The dynamic causality brain network reflects whether the working memory is solidified.

Working memory, which is foundational to higher cognitive function, is the "sketchpad of volitional control." Successful working memory is the inevitable outcome of the individual's active control and manipulation of thoughts and turning them into internal goals during which the causal brain processes information in real time. However, little is known about the dynamic causality among distributed brain regions behind thought control that underpins successful working memory. In our present study, given that correct responses and incorrect ones did not differ in either contralateral delay activity or alpha suppression, further rooting on the high-temporal-resolution EEG time-varying directed network analysis, we revealed that successful working memory depended on both much stronger top-down connections from the frontal to the temporal lobe and bottom-up linkages from the occipital to the temporal lobe, during the early maintenance period, as well as top-down flows from the frontal lobe to the central areas as the delay behavior approached. Additionally, the correlation between behavioral performance and casual interactions increased over time, especially as memory-guided delayed behavior approached. Notably, when using the network metrics as features, time-resolved multiple linear regression of overall behavioral accuracy was exactly achieved as delayed behavior approached. These results indicate that accurate memory depends on dynamic switching of causal network connections and shifting to more task-related patterns during which the appropriate intervention may help enhance memory.

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

EEG based emotion recognition by hierarchical bayesian spectral regression framework.

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

Feedback-related brain activity in individual decision: evidence from a gambling EEG study.

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.