Zero-shot visual question answering (VQA) requires models to reason over unseen image–question pairs without task-specific supervision, where performance is often undermined by incomplete visual grounding and unstable reasoning. Recent approaches attempt to mitigate this by using large language models to generate external knowledge conditioned on captions and questions. However, these methods typically operate in an open-loop manner, lacking mechanisms to regulate competing interpretations or correct misaligned knowledge. Inspired by the hippocampal pattern completion mechanism, which supports inference from partial observations through memory reactivation and competitive stabilization, we propose ARK-PC for zero-shot VQA, which formulates reasoning as a two-stage completion process. It first expands fragmented multimodal cues into multiple structured candidate hypotheses, then adaptively reinforces coherent candidates while suppressing inconsistent ones through iterative feedback. By coupling knowledge generation with competitive refinement, ARK-PC transforms open-loop inference into a closed-loop stabilization process, enabling robust reasoning under uncertainty without external supervision. Experiments on OK-VQA and A-OKVQA demonstrate consistent state-of-the-art zero-shot performance and strong generalization across diverse backbones, indicating that the improvements stem from the proposed framework rather than model scale.

Due to the intracranial volume conduction effects, high-dimensional multi-channel electroencephalography (EEG) features often contain substantial redundant and irrelevant information. This issue not only hinders the extraction of discriminative emotional representations but also compromises real-time performance. Feature selection has been established as an effective approach to address the challenges while enhancing the transparency and interpretability of emotion recognition models. However, existing EEG feature selection research overlooks the influence of latent EEG feature structures on emotional label correlations and assumes uniform importance across various channels, directly limiting the precise construction of EEG feature selection models for multi-dimensional affective computing. To overcome these issues, this paper proposes a channel-wise EEG feature selection (CWEFS) method for multi-dimensional emotion recognition. Inspired by the volume conduction effects, CWEFS models feature selection within a shared latent structure that captures a consensus representation across EEG channels. This consensus space is jointly learned with a latent semantic analysis of emotional labels to preserve local geometric structure. Furthermore, CWEFS incorporates adaptive channel-weight learning to automatically assess the contribution of each channel. Comprehensive experimental results, compared against nineteen popular feature selection methods, demonstrate that the EEG feature subsets chosen by CWEFS achieve optimal emotion recognition performance across six evaluation metrics.

Individuals with severe motor impairments face significant challenges operating conventional interfaces due to reliance on physical movement. While brain–computer interfaces (BCIs) offer alternative control pathways, traditional paradigms (SSVEP/P300) suffer from stimulus dependency, high latency, and cognitive fatigue, limiting real-world deployment. This study presents a novel asynchronous motor imagery-BCI system for hands-free robotic platform navigation, integrating cognitive ergonomics principles to enhance operator experience. We developed an intuitive four-command control paradigm using a cascaded classifier architecture, eliminating dependence on external triggers. Key innovations include: 1) sub-100 ms ultralow-latency pipeline via hybrid feature fusion and ensemble learning; 2) stimulus-independent operation leveraging endogenous sensorimotor cortex activation (μ/β-band); and 3) cognitive load-optimized interaction with ROS-based neurofeedback and NASA-TLX-validated ergonomic design. Evaluated on BCI Competition IV 2a dataset (nine subjects) and self-collected high-resolution electroencephalography data (seven subjects), the system achieved 72.15% offline classification accuracy and 95.36% online command execution success rate with 97ms mean computational latency. NASA-TLX evaluation revealed a 32% reduction in cognitive workload compared to synchronous paradigms. This work establishes a framework for cognitively enhanced mobility assistance, advancing practical brain-controlled assistive technologies.

Decoding visual stimuli from human brain activity is a fundamental challenge in cognitive neuroscience and neuroimaging. While recent advances in deep learning have significantly improved the performance of fMRI-to-image decoding, most existing methods overlook the issue of inter-subject variability in fMRI data, which leads to poor generalization across subjects. Current approaches often rely on partially shared model architectures that offer limited generalization and still require subject-specific components, restricting their applicability to unseen subjects. To address this limitation, we propose BrainX, a universal brain decoding framework that constructs a unified fMRI encoder and image generator to achieve subject-agnostic modeling. Specifically, we introduce a feature disentanglement mechanism that extracts subject-shared features from the fMRI embeddings, which are then fed into the image generator to reconstruct visual stimuli. This design eliminates the need for subject-specific models and significantly enhances cross-subject generalization. Additionally, we develop a neuro-geometric fMRI representation learning method that projects 3D cortical structures onto a 2D surface space, effectively mitigating the inaccuracies caused by imprecise geodesic distance estimation in 3D Euclidean space. Extensive experiments on the Natural Scenes Dataset (NSD) demonstrate that BrainX consistently outperforms existing state-of-the-art methods across three decoding settings: within-subject, cross-subject with finetuning, and cross-subject without finetuning.

Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.

This paper addresses the edge-consensus problem in a directed nodal network, where each edge is described by uncertain fractional-order dynamics subject to positivity constraints. To solve the issues of positivity and consensus in uncertain fractional-order networked systems (UFONSs) with the order 0q≤ 1, a distributed control protocol by virtue of state feedback is designed. Some sufficient criteria are established to ensure positive edge-consensus of UFONSs. Subsequently, to handle scenarios with unmeasurable states in practical systems, an observer-based control protocol is proposed to achieve positive edge-consensus for UFONSs. Furthermore, by leveraging the fractional-order stability theory and the properties of positive systems, some sufficient positive edge-consensus conditions that depend only on the number of edges and nodes are formulated instead of the global information of the network. Finally, two simulation examples are shown to verify the feasibility of the proposed protocols.

Research on emotion recognition in conversations emphasises the importance of complex relationships between conversational context and multimodality. Graph-based methods, particularly hypergraph-based methods have shown promise in capturing these relationships. However, challenges persist in avoiding redundant context while capturing essential information for optimal context embeddings and fully leveraging cross-modal complementarities for sufficient fusion. In contrast, the human brain flexibly retrieves relevant memories and integrates multi-modal data for accurate recognition. Based on this superiority, we propose BrainyHGNN, a brain-inspired hypergraph neural network. It integrates a Dynamic Memory Selector for contextual hyperedges, mimicking selective memory retrieval mechanisms for adaptive and modality-specific context retrieval. HierSensNet is designed for multi-modal hyperedges, mirroring hierarchical cross-modal interaction mechanisms to ensure effective multimodal fusion. Experimental results on two benchmark datasets validate the superior performance of BrainyHGNN, confirming the effectiveness of its innovative approach. This work highlights the potential of brain-inspired methods to advance flexible context retrieval and sufficient multimodal fusion, presenting a promising direction for future research in this domain.

Ensemble clustering, which combines the information from multiple base clusterings to obtain a better partition result, has received extensive attention due to its effectiveness and robustness. Although many algorithms have been developed in recent years that have achieved impressive results in practical applications, two challenging issues in ensemble clustering remain. First, most algorithms assume that all base clusterings have the same impact on the clustering results, assigning them the same weight. This makes the clustering performance susceptible to the influence of redundant, low-quality base clusterings. Second, co-association matrix-based algorithms often rely on additional methods, such as hierarchical agglomerative clustering, to obtain the final clustering result after constructing the weighted co-association matrix. This not only complicates optimization process but also leads to the loss of some sample-similarity information during clustering. To address this problem, we propose a novel Toward Balance Adaptive Weighted Ensemble Clustering (TBAWEC) algorithm. This method transforms the ensemble clustering problem into an optimization problem, producing the final result without requiring additional clustering algorithms. Moreover, we introduce balanced technology into ensemble clustering for the first time, significantly improving the balance of clustering results. Extensive experiments on real datasets demonstrate that the proposed algorithm outperforms the most advanced ensemble and balanced clustering algorithms simultaneously.

Motion planning, considered a fuzzy sequential decision-making problem, encounters significant challenges due to inherent environmental uncertainty. Traditional planning methods that rely on single strategies often struggle in complex scenarios. While fuzzy systems excel at handling uncertainty, high-dimensional continuous spaces require a large number of fuzzy rules, which significantly increases computational complexity. In contrast, humans leverage limited and fuzzy information to address various decision-making scenarios flexibly and efficiently. The concurrent reasoning mechanism in the prefrontal cortex plays a crucial role during this process. Consequently, the brain-inspired model and the concept of multiple fuzzy rules offer a novel perspective for the above issues. Motivated by these insights, this article proposes a brain-inspired motion planning method called harmonized learning with concurrent arbitration (HLCA). Specifically, inspired by the concurrent inference model, a concurrent arbitration module is employed in the planning process to effectively manage the boundary between exploration and exploitation. Furthermore, inspired by the multistrategy processing mechanism, HLCA introduces multistrategy harmonized learning by referring to the mechanism for operating multiple fuzzy rules, allowing the dynamic selection of strategies through a reliability function to enable self-improving learning. Experimental results demonstrate that HLCA outperforms state-of-the-art benchmarks, highlighting its potential to enhance the planning performance of robots by learning from the human brain.

Safety driver vigilance is a prerequisite for the safe operation of autonomous vehicles. In contrast to vehicle behavioral trajectory detection, which suffers from high latency and low accuracy, vigilance detection based on physiological signals is currently the most reliable and accurate method. While vigilance monitoring methods using electroencephalograms (EEG) have made considerable progress in experimental scenarios, they remain a challenging problem in scenario-constrained conditions, such as high-speed moving autonomous vehicles. This is due to the low signal-to-noise ratio in EEG signal acquisition and the difficulty of real-time processing. Moreover, cumbersome data acquisition processes and the challenges of labeling have hindered progress in this area. Given the successful use of EEG for monitoring in experimental settings, we believe that the transfer of knowledge learned from these scenarios to new contexts is reasonably feasible. Thus, this work aims to bridge the domain gap between experimental and real-world scenarios while balancing the number of channels and accuracy. Specifically, we propose a framework for EEG vigilance detection capable ofCross-scenario,Cross-subject, andCross-device, calledCCC. The proposed framework leverages the standard montage structure of EEG channels, reducing the number of channels by considering the common regions of EEG channels across different scenarios. The results show that our proposed model achieves an average accuracy of 86.20% on the SEED-VIG dataset with 12 subjects, which is higher than the 82.21% achieved by state-of-the-art deep learning approaches. Finally, we investigate the role of the attention mechanism and transfer learning, and further attempt to explain the advantages of our proposed approach from a visualization perspective.

Efficient and reliable motion planning is essential for robots in transportation systems. Learning-based motion planners, especially those leveraging graph neural networks (GNNs), have emerged as promising approaches to accelerate motion planning. However, current GNN-based planners suffer from inevitable performance bottlenecks due to their insufficient exploitation of environmental information and the intrinsic connections within graph structures. In contrast, the human brain exhibits innate strengths in decision-making and reasoning, enabling complex inferences from sparse observations and rapid integration of new information to control behavior. Neuroscience evidence reveals the human brain translates decision-making problems into graph structures and sensory observations, leveraging spatial relational memory for sensory inference. To address these issues, this paper proposes a brain-inspired GNN-based motion planner, BrainyMP, which innovatively draws on the brain’s spatial relational memory mechanisms. Specifically, a selective sampling strategy is proposed to reduce unnecessary exploration during the construction of the random geometric graph (RGG). Additionally, a Brainy Edge Selector is designed to filter inappropriate edges, enhancing planning quality. Furthermore, the Memory-aware Predictor is proposed to improve graph pattern learning capabilities and planning efficiency by integrating subgraph structures. Extensive experimental results demonstrate that the proposed method significantly improves planning quality and efficiency in maze and robotic-arm manipulation tasks while maintaining high success rates. These results indicate that emulating human brain mechanisms holds promise for improving robotic performance.

Due to the small size of valid samples, multi-source EEG features with high dimensionality can easily cause problems such as overfitting and poor real-time performance of the emotion recognition classifier. Feature selection has been demonstrated as an effective means to solve these problems. Current EEG feature selection research assumes that all dimensions of emotional labels are complete. However, owing to the open acquisition environment, subjective variability, and border ambiguity of individual perceptions of emotion, the training data in the practical application often includes missing information, i.e., multi-dimensional emotional labels of several instances are incomplete. The aforementioned incomplete information directly restricts the accurate construction of the EEG feature selection model for multi-dimensional emotion recognition. To wrestle with the aforementioned problem, we propose a novel EEG feature selection model with weighted self-expression learning (WSEL). The model utilizes self-representation learning and least squares regression to reconstruct the label space through the second-order correlation and higher-order correlation within the multi-dimensional emotional labels and simultaneously realize the EEG feature subset selection under the incomplete information. We have utilized two multimedia-induced emotion datasets with EEG recordings, DREAMER and DEAP, to confirm the effectiveness of WSEL in the missing multi-dimensional emotional feature selection challenge. Compared to nine state-of-the-art feature selection approaches, the experimental results demonstrate that the EEG feature subsets chosen by WSEL can achieve optimal performance in terms of six performance metrics.

A major challenge in motor imagery Brain-Computer Interfaces (MI-BCIs) arises from domain shift due to large individual differences. Currently, most cross-subject MI-BCI decoding methods rely on transfer learning to extract subject-shared features or align data distributions. However, these methods typically require all unlabeled data from the target subjects or labeled calibration data, which is unavailable in practical applications. To address this, we propose a brain lateralization-guided subject adaptive network, BLSAN, to enhance model generalization through local-global adversarial training. Specifically, two separate adversarial networks for left and right hemispheres are designed to reduce local differences, and features extracted from both hemispheres are combined for global adversarial training. Additionally, we design a confidence-based pseudo label generation method to enhance model discriminability. We validate the effectiveness of our approach on two public MI datasets, BCI Competition IV 2a and 2b, only with some unlabeled calibration data, which improves the practicality of MI-BCIs.

EEG emotion recognition plays a significant role in various mental health services. Deep learning-based methods perform excellently, but still suffer from interpretability. Although methods such as Gradient-weighted Class Activation Mapping(Grad-CAM) can cope with the above problem, their coarse granularity cannot accurately reveal the mechanism to promote emotional intelligence. In this paper, fine-grained interpretability is proposed, called Concat-aided Grad-CAM. Specifically, the multi-level feature mapping before the fully connected layer is concatenated to obtain the gradients of the target concept so that the discriminant information can be directly located in the high-precision area. Unlike coarse-grained interpretability methods applied in EEG emotion recognition, it can accurately highlight the EEG channels related to emotion rather than an obscure area. In addition, a systematic brain functional network is proposed to reveal the relationship between those channels and to further improve emotion recognition performance. The channels with greater contributions are connected, and those connections are learned by dynamic graph convolutional networks, while the others are independent to eliminate interference. Experiments on two EEG emotion recognition datasets manifest that Concat-aided Grad-CAM can be interpreted by the fine-grained. In addition, it has been shown that the learned brain functional network can improve the performance of the baselines. Significantly, the experiment results achieve state-of-the-art performance in subject-dependent experiments.

Emotions play an essential role in affecting the performance of cognitive abilities in continuous cognitive tasks. Most previous studies share a common issue in that the evoked emotions are simply presumed to be real emotions, without taking into account the observation that emotions may be changed when carrying out cognitive activities. This may lead to the inaccurate detection of true emotions, which further adversely affects the investigation of interactions between emotion and cognition. To address this challenging problem, the present work develops an innovative study using EEG measurement to investigate the interaction between dynamic affection and cognitive ability. In particular, a real-time emotion detection model by the use of physiological signals (i.e., EEG) is constructed, to dynamically monitor the current emotional state. Given the observed emotion, the analysis of the interaction between cognitive abilities and dynamic emotions is undertaken from the perspectives of both behavioral performance and brain mechanisms. Research outcomes indicate that emotions are not stable, and are indeed dynamically changed by cognitive performance. Meanwhile, cognitive activities also influence the brain activation pattern revealed under different emotions, which validates the necessity of introducing the dynamic emotion monitoring model. In addition, the best performance has been found when the emotional state is neutral in terms of accuracy and response time. The results of this study provide a potential basis for assessing the cognitive abilities of individuals with different emotions in a variety of applications of cognitive scenarios.

Cross-site datasets expand the data size and could improve the disease diagnosis capabilities of machine learning models. However, differences in data distribution between different sites can lead to poor model generalizability. Although transfer learning is a mainstream method often used to tackle the issue, most transfer learning studies assume that all the target samples are given in the training procedure, which is not available in clinical applications where the target samples arrive sequentially. Online transfer learning (OTL) aims to accomplish clinical diagnostic tasks by adaptively updating the ensemble model containing source and target classifiers. However, OTL is limited by zero initialization and requires numbers of samples to iterate. This results in the underperformance of OTL in clinical applications where few samples can be obtained. In this article, we propose a new framework named few-shot synthetic OTL (FSOTL) to address this issue. FSOTL uses synthetic data to warm up the model in an online fashion. It not only alleviates the problem of scarcity of samples in the target domain but also enables the model to gain more knowledge. Our experiments show that FSOTL performs more stably and achieves more accurate results with few target samples, thereby offering a promising cross-site online computer-aided diagnosis system for large-scale applications.

Although advances in deep learning technologies have greatly facilitated the brain intention decoding from electroencephalogram (EEG) in motor imagery brain–computer interfaces (MI-BCIs), significant individual differences hinder the practical cross-subject MI-BCI applications. Unlike other existing domain adversarial transfer networks that focus on designing different discriminators to reduce individual differences, inspired by the motor lateralization phenomenon, we innovatively utilize transformer and the spatiotemporal pattern differences of EEG as prior knowledge to enhance the feature discriminability in our brain decoding adversarial network. In addition, to address adversarial network decision boundaries bias toward the source domain, we propose a data augmentation method, EEGMix to rapidly mix and enrich the target domain data. With an adaptive adversarial factor, our decoding model reduces the differences in marginal and conditional distribution simultaneously. Three public MI datasets, 2a, 2b, and OpenBMI verified our model's effectiveness. The accuracy achieved 77.49%, 85.19%, and 79.37%, superior to other state-of-the-art algorithms.

Neural decoding aims to extract information from neurons' activities to reveal how the brain functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio-temporal computing has become a hot topic for neural decoding. However, the extant spatio-temporal decoding methods usually use static brain topology, ignoring the dynamic patterns of the interaction between brain regions. Further, they do not identify the hierarchical organization of brain topology, leading to only superficial insight into brain spatio-temporal interactions. Therefore, here we propose a novel framework, the Multi-Scale Spatio-Temporal framework with Adaptive Brain Topology Learning (MSST-ABTL), for neural decoding. It includes two new capabilities to enhance spatio-temporal decoding: i) ABTL module, which learns dynamic brain topology while updating specific patterns of brain regions, ii) MSST module, which captures the association of spatial pattern and temporal evolution, and further enhances the interpretability of the learned dynamic topology from multi-scale perspective. We evaluated the framework on the public Human Connectome Project (HCP) dataset (resting-state and task-related fMRI data). The extensive experiments show that the proposed MSST-ABTL outperforms state-of-the-art methods on four evaluation metrics, and also can renew the neuroscientific discoveries in the brain's hierarchical patterns.

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96.05% under the binary classification(cooperation vs competition).

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

A common drawback of EEG-based emotion recognition is that volume conduction effects of the human head introduce interchannel dependence and result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide extra useful information, and they actually reduce the performance of emotion recognition. However, the existing feature selection methods, commonly used to remove redundant EEG features for emotion recognition, ignore the correlation between the EEG features or utilize a greedy strategy to evaluate the interdependence, which leads to the algorithms retaining the correlated and redundant features with similar feature scores in the EEG feature subset. To solve this problem, we propose a novel EEG feature selection method for emotion recognition, termed global redundancy minimization in orthogonal regression (GRMOR). GRMOR can effectively evaluate the dependence among all EEG features from a global view and then select a discriminative and nonredundant EEG feature subset for emotion recognition. To verify the performance of GRMOR, we utilized three EEG emotional data sets (DEAP, SEED, and HDED) with different numbers of channels (32, 62, and 128). The experimental results demonstrate that GRMOR is a promising tool for redundant feature removal and informative feature selection from highly correlated EEG features.

Functional magnetic resonance imaging (fMRI) is a methodology for measuring human brain activities. It has become more and more popular in neural decoding due to its noninvasive. Neural decoding aims to establishing models to reconstruct external stimuli or features of stimuli from known brain responses, so that we can understand the principles of brain functions such as emotion, cognition and language. Neural decoding based on fMRI is of great significance for further understanding the mechanism of brain operation. Most existing studies take multi-scale topology information of brain networks obtained from fMRI into account in neural decoding. However, they always ignore the simultaneous modeling of network structure and hemodynamic response, thus leading to information loss. In addition, current multi-scale methods usually only utilize spatial or logical reasoning relationship of brain networks, which brings challenge to precise neural decoding. In this work, we present a novel and robust multi-scale spatial and logical reasoning learning framework (MSLR) for fMRI-based neural decoding. Specifically, we first design graph signal wavelet generation module to combine brain network topology and node information to construct multi-scale representation of brain networks in a local to global manner. Then, we develop multi-scale information fusion module that can simultaneously model the spatial and logical reasoning relationship of brain networks, it can also learn discriminative multi-scale features with brain state transition. Finally, we construct a neural decoding module to predict the brain states. We evaluated the framework on the public Human Connectome Project (HCP) dataset that included 986 participants. The experimental results with support vector machine (SVM) outperform current state-of-the-art methods on four evaluation metrics (accuracy: 91.58, kappa coefficient: 0.883, macro F1: 0.865 and hamming distance: 0.105) under 19 different stimuli spanning 7 different cognitive tasks. The interpretation of the learned multi-scale representation replicates neuroscientific findings from previous fMRI studies and renews the multi-scale information flow pattern of brain network in neural decoding.

Anxiety is a complex emotional state that has a great impact on people's physical and mental health. Effectively identifying different anxiety states is very important. By inducing various anxiety states of 12 healthy college students with electroencephalograph (EEG) recording, comprehensive EEG features, including not only commonly used frequency domain features but also the time domain, statistical and nonlinear features were extracted from different EEG bands and brain locations. Next, correlation analysis was performed between various features and anxiety level changes that were predetermined at each stage of the experiment using a 5-point Likert scale, and the most relevant features were collected. Then, different classifiers were applied to classify four anxiety levels using different features alone or together to explore their anxiety recognition ability. Based on our dataset, the highest accuracy of identifying four anxiety states reached approximately 62.56 percent using the Support Vector Machine (SVM), which improved the classification accuracy compared with previous studies. The results also revealed the importance of EEG linear features (especially for features including total power, mean square and variance) in anxiety recognition. Furthermore, it suggested that EEG features in the beta band and the frontal lobe contributed to anxiety recognition more than the features in the other bands or other brain locations. In short, this study improves the accuracy of multi-level anxiety recognition and helps in choosing better features for anxiety recognition, which lay the foundation for the detection of continuous anxiety changes.

In this article, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of features extracted from sensor EEG signals are used for emotion recognition. However, they cannot unambiguously describe the location of emotions associated with neural activities and information propagation or the interaction between brain regions. In addition, most current studies use low-density sensor EEG signals. Moreover, source signals estimated from high-density sensor EEG signal have not been employed for emotion recognition to date. We designed a BDC network-based framework using EEG source signals to investigate emotion recognition. The global cortex factor-based multivariate autoregressive (GCF-MVAR) method was utilized to extract emotion-related BDC features. Our study revealed that the combined BDC and DE features facilitated a recognition accuracy of up to 89.58 percent, which is higher than the rate obtained from BDC features and DE features alone. The sensor features derived from high-density EEG signals also exhibited higher recognition accuracy compared to low-density EEG signals. These findings suggest that BDC features derived from EEG source signals can better characterize human emotional states and are meaningful for emotion recognition.

Feature selection has attracted a lot of attention in obtaining discriminative and non-redundant features from high-dimension data. Compared with traditional filter and wrapper methods, embedded methods can obtain a more informative feature subset by fully considering the importance of features in the classification tasks. However, the existing embedded methods emphasize the above importance of features and mostly ignore the correlation between the features, which leads to retain the correlated and redundant features with similar scores in the feature subset. To solve the problem, we propose a novel supervised embedded feature selection framework, called feature selection under global redundancy minimization in orthogonal regression (GRMOR). The proposed framework can effectively recognize redundant features from a global view of redundancy among the features. We also incorporate the large margin constraint into GRMOR for robust multi-class classification. Compared with the traditional embedded methods based on least square regression, the proposed framework utilizes orthogonal regression to preserve more discriminative information in the subspace, which can help accurately rank the importance of features in the classification tasks. Experimental results on twelve public datasets demonstrate that the proposed framework can obtain superior classification performance and redundancy removal performance than twelve other feature selection methods.

Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more appropriate in fitting the real inherent properties of the brain function activity pattern, which can further limit noise interference to improve the accuracy of the estimated result. Recently, the neuroscientists widely suggested that the inherent brain function activity pattern indicates sparse, modular and overlapping topology. However, previous studies have never considered this factual characteristic. Thus, we propose a novel method by integration of these inherent brain function activity pattern priors to estimate FC. Extensive experiments on synthetic data demonstrate that our method can more accurately estimate the FC than previous. Then, we applied the estimated FC to predict the symptom severity of depressed patients, the symptom severity is related to subtle abnormal changes in the brain function activity, a more accurate FC can more effectively capture the subtle abnormal brain function activity changes. As results, our method better than others with a higher correlation coefficient of 0.4201. Moreover, the overlapping probability of each brain region can be further explored by the proposed method.

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.

Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square regression, FSOR, utilizing orthogonal regression, can preserve more discriminative information in the subspace and have better performance on feature selection. However, the embedded approaches have scarcely considered the dependency among the selected feature subset. To address the defect, in this paper, we propose a two-stage (filter-embedded) feature selection technique based on Maximum Relevance Minimum Redundancy and FSOR, termed as Orthogonal Regression with Minimum Redundancy (ORMR). We compared the feature selection performance between ORMR and nine other state-of-the-art supervised feature selection methods on six benchmark datasets. The results demonstrate the advantage of ORMR method over others in choosing discriminative features with considering the redundant information among the selected feature subset.

A common drawback of the EEG applications is that the volume conduction of human head leads to lots of redundant information in EEG recordings. To reduce the redundancy and choose informative EEG features, in this paper, we propose an EEG feature selection technique, termed as Feature Selection with Orthogonal Regression (FSOR). Compared with classical feature selection methods, for nonlinear and nonstationary EEG signals, FSOR can employ orthogonal regression to preserve more discriminative information in the subspace. To verify the EEG feature selection performance, we collected a multichannel EEG dataset for emotion recognition and compared FSOR with two popular feature selection methods. The experimental results demonstrate the advantage of FSOR method over others for reducing the redundant information among the EEG relevant features. Additionally, we found that the absolute power ratio of beta wave to theta wave is the most discriminative feature, and beta band is the critical band for emotion recognition.

Identifying and evaluating functionally connected regions in the brain has become a challenging problem to solve in many studies of neurological and psychiatric disorders. In particular, functional connectivity of brain mechanisms underlying disturbed cognition in depression is still not well understood in current neuroscience research. Based on the Stroop paradigm, specifically, the face-word Stroop task, we aimed to analyze task-based electroencephalography (EEG) functional connectivity in subjects with depression and in healthy controls, using concepts from time series clustering. In this study, EEG signals of 10 healthy subjects and 10 depressive patients were collected. Then EEG signals were segmented into temporal window corresponding to the event-related potentials (ERPs). For each duration, hierarchical clustering (HC) along with optimizations for the dynamic time warping (DTW) were employed to identify meaningful functionally connected regions and examine changes in depression. It was demonstrated that our method had the potential to become a useful tool for clinical investigators to identify the underlying impairments of brain functional connections in the patients with depression. One of the most representative functional connections obtained in the present study indicated that during the N450 component, the left and right frontal brain parts may discriminate depressive patients from healthy controls. This finding was interpreted by valence-hypothesis, which can prove the validity of the theory of emotional conflict in major depression.

Depression is a mental disorder of high prevalence, leading to a negative effect on individuals, their families, society and the economy. In recent years, the problem of automatic detection of depression from the speech signal has gained more interest. In this paper, a new multiple classifier system for depression recognition was developed and tested. The novel aspect of this methodology is the combination of different speech types and emotions. First of all, using a sample of 74 subjects (37 depressed patients and 37 healthy controls), we examined the discriminative power of different speech types (interview, picture description, and reading) and speech emotions (positive, neutral, and negative). Some voice features (e.g. short time energy, intensity, loudness, zero-crossing rate (ZCR), F0, jitter, shimmer, formants, mel frequency cepstral coefficients (MFCC), linear prediction coefficient (LPC), line spectrum pair (LSP), and perceptual linear predictive coefficients (PLP)) were tested. Then, a new multiple classifier method was proposed to detect depression. It was observed that the overall recognition rate using interview speech was higher than employing picture description speech and reading speech. Furthermore, neutral speech showed better performance than positive and negative speech. Among these features, short time energy, ZCR, LPC, MFCC and LSP were the robust features that gave high accuracy in different types of speech. Finally, this new approach showed a high accuracy of 78.02%, giving high encouragement for detecting depression in speech.

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.