Bin Hu

Bin Hu
Lanzhou University | LZU · School of Information Science and Engineering

Professor

About

503
Publications
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9,932
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Publications

Publications (503)
Article
Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer’s disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to...
Article
Background: Healthy aging is usually accompanied by alterations in brain network architecture, influencing information processing and cognitive performance. However, age-associated coordination patterns of morphological networks and cognitive variation are not well understood. Purpose: To investigate the age-related differences of cortical topol...
Article
Background Cannabis is the most frequently used illicit drug worldwide. Although multiple structural MRI studies of heavy cannabis use (CB) have been undertaken, the reports of the volume alterations in the amygdala, hippocampus, and pallidum are not consistent. This study aims to detect subregion-level morphological alterations, analyze the correl...
Article
Depression is a global psychological disease that does serious harm to people. Traditional diagnostic method of the doctor-patient communication, is not objective and accurate enough. Thus, a more accurate and objective method for depression detection is urgently needed. Resting-state electroencephalography (EEG) can effectively reflect brain funct...
Article
Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology...
Article
This paper focuses on the state bounding problem for the time-delay impulsive and switching genetic regulatory networks (ISGRNs) with exogenous disturbances. Firstly, a sufficient criterion for the state bounding is obtained such that all the trajectories of ISGRNs under consideration converge exponentially into a sphere on the basis of an average...
Article
Full-text available
Objectives Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated. Methods Nineteen-channel resting-state eyes-closed EEG s...
Article
Full-text available
According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applicat...
Article
Full-text available
The enlargement of ventricular volume is a general trend in the elderly, especially in patients with Alzheimer’s disease (AD). Multiple susceptibility loci have been reported to have an increased risk for AD and the morphology of brain structures are affected by the variations in the risk loci. Therefore, we hypothesized that genes contributed sign...
Article
Objective Recent neuroimaging studies have demonstrated that burnout is linked to specific anatomical and functional abnormalities in the brain. However, topological alterations of brain networks are not yet characterized in burnout. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 32 female participants with...
Article
Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer's disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore...
Article
Full-text available
Entropy is a measurement of brain signal complexity. Studies have found increased/decreased entropy of brain signals in psychiatric patients. There is no consistent conclusion regarding the relationship between the entropy of brain signals and mental illness. Therefore, this meta-analysis aimed to identify consistent abnormalities in the brain sign...
Article
Studying brain aging improves our understanding in differentiating typical and atypical aging. Directly utilizing traditional morphological features for brain age estimation did not show significant performance in healthy controls (HCs), which may be due to the negligence of the information of structural similarities among cortical regions. For thi...
Article
In recent years, mental health (especially depression) of university students has aroused general concern. Fast detection of students at risk of depression using multimedia data is a challenge. However, existing methods require the cooperation of participants such as using their speech or facial expression, which are inconvenient to collect and dif...
Article
In information retrieval (IR), the improvement of the effectiveness often sacrifices the stability of an IR system. To evaluate the stability, many risk-sensitive metrics have been proposed. Since the theoretical limitations, the current works study the effectiveness and stability separately, and have not explored the effectiveness–stability tradeo...
Article
Major depressive disorder (MDD) may be driven by dysfunction in intrinsic dynamic properties of the brain, and EEG microstate is a promising method for analyzing brain dynamics. However, the alterations in EEG microstate is still not entirely clear, and its ability for MDDs detection is worth probing. Moreover, the mechanism behind the neural netwo...
Article
Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear netwo...
Article
It was observed that accuracy of the Subject-Dependent emotion recognition model was much higher than that of the Subject-Independent modelin the field of electroencephalogram (EEG) based affective computing. This phenomenon is mainly caused by the individual difference of EEG, which is the key issue to be solved for the application of emotion reco...
Article
The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the monitoring, diagnosis and treatment of epilepsy. In this study, an intelligent detection framework with the discriminative Stein kernel-based sparse representation (DSK-SR) is constructed to distinguish epileptic EEG signals. Specifically, in the scheme of DSK-...
Article
Chunk decomposition, which requires the mental representation transformation in accordance with behavioral goals, is of vital importance to problem solving and creative thinking. Previous studies have identified that the frontal, parietal, and occipital cortex in the cognitive control network selectively activated in response to chunk tightness, ho...
Article
We focus on unsupervised representation learning for skeleton based action recognition. Existing unsupervised approaches usually learn action representations by motion prediction but they lack the ability to fully learn inherent semantic similarity. In this paper, we propose a novel framework named Prototypical Contrast and Reverse Prediction (PCRP...
Chapter
Recently, Electroencephalography (EEG) is wildly used in depression detection. Researchers have successfully used machine learning methods to build depression detection models based on EEG signals. However, the scarcity of samples and individual differences in EEG signals limit the generalization performance of machine learning models. This study p...
Preprint
Full-text available
On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single time-frequency domain method method, but the single time-frequency domain method is too simple and is not conducive to simulating the complex link relationship between brain...
Article
Currently, depression has become a common mental disorder, especially among postgraduates. It is reported that postgraduate students have a higher risk of depression than the general public, and they are more sensitive to contact with others. Thus, a non-contact and effective method for detecting people at risk of depression becomes an urgent deman...
Article
A wideband wearable metasurface antenna with dual-mode (on-body and off-body mode), dual-band, designed for the 2.45 and 5 GHz wireless body area network (WBAN), is proposed in this communication. The TM11 mode of the circular patch is used for the off-body mode in the 2.45 GHz WBAN band because of its broadside radiation pattern, meanwhile, the TM...
Article
This article investigates the controllability for a class of piecewise nonlinear impulsive non-autonomous systems. The problem is addressed by considering the nonlinearities and impulses as perturbations. First, a standard framework is introduced to transform the issue of controllability to the existence of a fixed point by designing a proper admis...
Article
Full-text available
A metasurface (MS) antenna for the 5 GHz wireless local area network (WLAN) band is presented in this paper. The antenna is feed by a modified aperture‐coupled structure with an optimized rectangular nonuniform MS to widen the bandwidth and improve the gain. The proposed antenna has a thickness of 4 mm and occupies an area of 50 mm × 50 mm. The ope...
Article
Full-text available
With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent inform...
Article
Full-text available
In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two sub...
Article
Full-text available
Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer’s disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We de...
Article
These days, physiological signals have been studied more broadly for emotion recognition to realize emotional intelligence in human-computer interaction. However, due to the complexity of emotions and individual differences in physiological responses, how to design reliable and effective models has become an important issue. In this article, we pro...
Article
Rationale/Importance Researches have highlighted communication deficits between resting-state brain networks in major depressive disorder (MDD), as reflected in abnormal functional connectivity (FC). However, it is unclear whether impaired FC is associated with MDD pathology or is simply incidental to MDD symptoms. Moreover, there is no generalized...
Article
Objective: The excellent Signal-to-Noise Ratio (SNR) is the premise of Electroencephalogram (EEG) research and applications. This study aims to use innovative method to swiftly remove the Ocular Artifacts (OAs) from multichannel EEG to enhance the SNR. Methods: The moment matching method which is prevalently used to removing stripe noise from hy...
Article
This article is concerned with the collective behaviors of discrete-time multi-agent systems with single-integrator dynamics under general signed digraphs, in which each agent iteratively updates its own state based upon the current relative states between itself and its neighbors. By using graph-theoretic, matrix-theoretic, and control-theoretic t...
Article
Background: The latest studies have considered the time-dependent structures in dynamic brain networks. However, the effect of periphery structures on the temporal flow of information remains unexplored in patients with major depressive disorder (MDD). In this work, we aimed to explore the pattern of interactions between brain regions in MDD acros...
Conference Paper
Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fu...
Article
Neuroimaging studies have indicated that the altered functional connectivity (FC) of the subgenual anterior cingulate cortex (sgACC) might be potential pathophysiology of major depressive disorder (MDD). However, directed connectivity is proven to be more closely to neurophysiological processes underlying brain activity than FC. This study aimed to...
Article
Background/aims: Irritable bowel syndrome (IBS) is a prevalent functional gastrointestinal disease characterized by recurrent abdominal pain and bowel dysfunction. However, the majority of previous neuroimaging studies focus on brain structure and connections but seldom on the inter-hemispheric connectivity or structural asymmetry. This study uses...
Preprint
Full-text available
Person re-identification via 3D skeletons is an emerging topic with great potential in security-critical applications. Existing methods typically learn body and motion features from the body-joint trajectory, whereas they lack a systematic way to model body structure and underlying relations of body components beyond the scale of body joints. In th...
Article
Full-text available
At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposit...
Preprint
Full-text available
Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fu...
Article
Bipolar I disorder (BD-I) is associated with high-risk behaviors, such as suicide attempts and addictive substance abuse. Understanding brain activity exposure to risk decision making provides evidence for the treatment of BD-I patients. This study aimed to investigate the temporal dynamics of brain connectivity underlying risk decision making in p...
Article
5G technology brings a comprehensive improvement in the network layer, which meets real-time, high-efficiency, and stability requirements in medical scenarios to a large extent, such as remote diagnosis and surgery. The heavy burden and severe impact of mental disorders make it desirable to find quantitative and automatic assessment approaches for...
Article
Full-text available
Does cognitive style have influence functions? In this study, we build on the existing literature (e.g., McKone et al., 2010; Hakim et al., 2016) in providing answers to the question. We anticipated that the type of attitude tendency (i.e., holistic or analytic, Nisbett et al., 2001) is somehow related to the perceptual processing ability applied i...
Article
The rapid development of the COVID-19 pandemic has threatened the lives of people around the world. Many people were caught in anxiety and panic, which also prevents people from fully concentrating on their normal lives. However, the current common neurofeedback therapies used to solve the problem of lack of attention cannot fully deal with the dif...
Article
Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmi...
Chapter
The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to re...
Article
This paper investigates the problem of bipartite consensus tracking for a class of linear singular multi-agent systems under a signed graph topology, where the control input of each agent is subject to saturation. By exploiting a parametric algebraic Riccati equation (ARE)-based low-gain feedback approach, a static state feedback control protocol a...
Article
Full-text available
Action recognition via 3D skeleton data is an emerging important topic. Most existing methods rely on hand-crafted descriptors to recognize actions, or perform supervised action representation learning with massive labels. In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that exploits different augmen...
Article
Full-text available
The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for the early detection of potential depression risk. A depression-related dataset is also criti...
Article
Full-text available
The Internet of Medical Things (IoMT) aims to exploit the Internet of Things (IoT) techniques to provide better medical treatment scheme for patients with smart, automatic, timely, and emotion-aware clinical services. One of the IoMT instances is applying IoT techniques to sleep-aware smartphones or wearable devices’ applications to provide better...
Preprint
Full-text available
Background: Vocal features have been proposed as a way to identify depression by distinguishing depression from healthy controls, but while there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. In particular, it is not clear whether vocal features are c...
Article
In recent years, graph convolutional neural networks have become research focus and inspired new ideas for emotion recognition based on EEG. Deep learning has been widely used in emotion recognition, but it is still challenging to construct models and algorithms in practical applications. In this paper, we propose a novel emotion recognition method...
Article
Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This paper presents a novel method for seizure detection using the Stein kernel-based sparse representation (SR) for EEG recordings. Different from the traditional SR schem...
Article
Objective: Electroencephalogram (EEG) based emotion recognition mainly extracts traditional features from time domain and frequency domain, and the classification accuracy is often low for the complex nature of EEG signals. However, to the best of our knowledge, the fusion of event-related potential (ERP) components and traditional features is not...
Article
Background: The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their interaction effects h...
Article
Full-text available
The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. I...
Article
Welcome to the first issue of IEEE Transactions on Computational Social Systems (TCSS) of 2021. I am excited to serve as the new Editor-in-Chief (EiC) of our journal. First, I would like to thank my predecessor, Prof. Fei-Yue Wang from the Chinese Academy of Science, for his dedication and commitment over the past three years. Under his outstanding...
Article
Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this paper, we investigated abnormal neural b...
Conference Paper
Although more and more researchers pay attention to the emotion classification, traditional emotion classification methods can not embrace changes in the global and local areas of the human brain after being stimulated. We propose an emotion classification method based on SVM combining brain functional connectivity. Firstly, the nonlinear phase-loc...