A5101753289- EEG and Brain-Computer Interfaces
- Anomaly Detection Techniques and Applications
- Time Series Analysis and Forecasting
- Gaze Tracking and Assistive Technology
- Traffic Prediction and Management Techniques
- Sustainable Supply Chain Management
- Neuroscience and Neural Engineering
- Context-Aware Activity Recognition Systems
- Human Pose and Action Recognition
- Functional Brain Connectivity Studies
- ECG Monitoring and Analysis
- Emotion and Mood Recognition
- Supply Chain and Inventory Management
- Non-Invasive Vital Sign Monitoring
- Stock Market Forecasting Methods
- Nanoplatforms for cancer theranostics
- Environmental Sustainability in Business
- Tryptophan and brain disorders
- Forecasting Techniques and Applications
- Transportation Planning and Optimization
- Thyroid Cancer Diagnosis and Treatment
- Advanced Memory and Neural Computing
- Ubiquitin and proteasome pathways
- Graphene research and applications
- Combustion and Detonation Processes
Aalborg University
2020-2025
Stanford University
2022-2025
China University of Mining and Technology
2025
Nankai University
2025
Hunan University
2025
Hunan Normal University
2025
Stratford University
2025
Beijing Jiaotong University
2022-2024
Nanjing Forestry University
2024
University of California, Davis
2020-2023
Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage labeled data inherently calls for a plethora semisupervised methods, and one most challenging common issues with is imbalanced distribution over classes. Although problem has long existed broad real-world HAR applications, it rarely explored literature. In this paper, we propose model from multimodal wearable sensory data. We aim to address not only challenges...
Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world exclusively brain intentions. Electroencephalography (EEG)-based BCI one of promising solutions due its convenient and portable instruments. Despite extensive research EEG in recent years, it still challenging interpret signals effectively nature noise difficulties capturing inconspicuous relations between specific activities. Most existing works either only consider as chain-like...
The vast proliferation of sensor devices and Internet Things enables the applications sensor-based activity recognition. However, there exist substantial challenges that could influence performance recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness many areas, plenty methods have been investigated to address In this study, we present a survey state-of-the-art for human We first introduce multi-modality sensory data provide information...
Feature extraction and classification play an important role in brain–computer interface (BCI) systems. In traditional approaches, methods pattern recognition field are adopted to solve these problems. Nowadays, the deep learning theory has developed so fast that researchers have employed it many areas like computer vision speech recognition, which achieved remarkable results. However, few people introduce method into study of biomedical signals, especially EEG signals. this paper, a wavelet...
The electroencephalogram (EEG) signal is a medium to realize brain-computer interface (BCI) system due its zero clinical risk and portable acquisition devices. Current EEG-based BCI research usually requires subject-specific adaptation step before can be employed by new user. In contrast, the subject-independent scenario, where well trained model directly applied users without precalibration, particularly desired. Considering this critical gap, focus in letter developing an effective EEG...
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due their convenient and portable instruments. Despite extensive research of EEG in recent years, it still challenging interpret signals effectively massive noises (e.g., low signal-noise ratio incomplete signals), difficulties capturing inconspicuous relationships between certain...
Motor imagery classification from EEG signals is essential for motor rehabilitation with a Brain-Computer Interface (BCI). Most current works on this issue require subject-specific adaptation step before applied to new user. Thus the research of directly extending pre-trained model users particularly desired and indispensable. As brain dynamics fluctuate considerably across different subjects, it challenging design practical hand-crafted features based prior knowledge. Regarding gap, paper...
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting EEG signals of their brains interact devices such as wheelchairs and intelligent robots. More specifically, motor imagery (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for variety BCI applications. Accurate classification MI-EEG while essential effective operation systems challenging due significant noise inherent...
Abstract Chronic ethanol exposure (CEE), which can lead to neuroinflammation, is an increasing risk factor for depression disorder, but the underlying mechanism not clear. Recent observations have revealed associations among psychiatric disorders, and alterations of gut microbiota. Here, we found that CEE induced depressive-like behavior, could be alleviated by probiotics transferred from donor recipient mice fecal microbiota transplantation (FMT). Neuroinflammation activation NLRP3...
Emotion recognition based on electroencephalography (EEG) has attracted significant attention due to its wide range of applications, especially in Human-Computer Interaction(HCI). Previous research treats different segments EEG signals uniformly, ignoring the fact that emotions are unstable and discrete during an extended period. In this paper, we propose a novel two-step spatial-temporal emotion framework. First, considering human not only "short-term continuity" but also "long-term...
Cyber-physical system sensors emit multivariate time series (MTS) that monitor physical processes. Such generally capture unknown numbers of states, each with a different duration, correspond to specific conditions, e.g., "walking" or "running" in human-activity monitoring. Unsupervised identification such states facilitates storage and processing subsequent data analyses, as well enhances result interpretability. Existing state-detection proposals face three challenges. First, they...
An EEG-based Brain-Computer Interface (BCI) is a system that enables user to communicate with and intuitively control external devices solely using the user's intentions. Current BCI research usually involves subject-specific adaptation step before ready be employed by new user. However, subject-independent scenario, in which well-trained model can directly applied users without pre-calibration, particularly desirable yet rarely explored. Considering this critical gap, our focus paper...
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for number significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home healthcare domains. Existing techniques mostly focus on binary single person, which limits their deployment wider complex practical scenarios. Therefore, multi-person multi-class has obtained popularity recently. Another challenge faced by the low accuracy due to...
Abstract Bcl-2 associated athanogene 3 (BAG3) is an important molecule that maintains oncogenic features of cancer cells via diverse mechanisms. One the functions assigned to BAG3 implicated in selective macroautophagy/autophagy, which attracts much attention recently. However, mechanism underlying regulation autophagy by has not been well defined. Here, we describe enhances promotion glutamine consumption and glutaminolysis. Glutaminolysis initiates with deamination glutaminase (GLS),...
Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while become increasingly complex computationally intensive, they struggle accuracy. Pursuing a different direction, this study aims instead enable much more efficient, lightweight that preserve being able be deployed on resource-constrained...
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS is uncover temporal dynamics spatial correlations among series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated forecasting, where design an optimal deep learning architecture automated, accuracy that surpasses what has been...
Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning methods offer better efficiency and accuracy than non-learning solutions, they are still immature in trajectory embedding suffer from poor generality heavy preprocessing for training. Targeting these limitations, we propose novel framework named KGTS based on knowledge graph grid embedding, prompt unsupervised contrastive improved computation....
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due their convenient and portable instruments. Motor imagery EEG (MI-EEG) kind of most widely focused signals, which reveals subjects movement intentions without actual actions. Despite extensive research MI-EEG in recent years, it still challenging interpret signals effectively massive...
Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI perform for reach and grasp task still difficulties, especially in continuous multidimensional of tactile feedback. In this research, system with feedback was proposed. Firstly, mental...
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit that capture interconnected processes. Solutions based on deep learning deliver state-of-the-art CTS performance employ a variety of spatio-temporal (ST) blocks are able to model temporal dependencies and spatial correlations among series. However, two challenges remain. First, ST-blocks designed manually, which is consuming costly. Second, existing models simply stack...