A5004432231- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- ECG Monitoring and Analysis
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Gaze Tracking and Assistive Technology
- Functional Brain Connectivity Studies
- Brain Tumor Detection and Classification
- Muscle activation and electromyography studies
- Neural dynamics and brain function
- Epilepsy research and treatment
- Neonatal and fetal brain pathology
- Neural Networks and Applications
- Non-Invasive Vital Sign Monitoring
- Human Pose and Action Recognition
- earthquake and tectonic studies
- Anomaly Detection Techniques and Applications
- Cardiac electrophysiology and arrhythmias
- Robotics and Sensor-Based Localization
- Stroke Rehabilitation and Recovery
- Advanced Neural Network Applications
- Medical Imaging and Analysis
- Tensor decomposition and applications
- Sparse and Compressive Sensing Techniques
- Earthquake Detection and Analysis
Shandong Academy of Sciences
2018-2025
Qilu University of Technology
2016-2025
Shandong Agricultural University
2021-2025
Henan University
2025
Tianjin University
2025
First Affiliated Hospital of Anhui Medical University
2024
Anhui Medical University
2024
Anhui Institute of Architectural Research and Design
2024
China University of Petroleum, Beijing
2024
Chang'an University
2024
The motor imagery brain–computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore function stroke patients. deep learning algorithms in MI-BCI require lots training samples, but electroencephalogram (EEG) data patients quite scarce. Therefore, expansion EEG has become an important part clinical research. In this paper, a convolution generative adversarial network (DCGAN) model proposed generate artificial further...
Th2-high asthma is characterized by elevated levels of type 2 cytokines, such as interleukin 13 (IL-13), and its prevalence has been increasing worldwide. Ferroptosis, a recently discovered programmed cell death, involved in the pathological process asthma; however, underlying mechanisms remain incompletely understood. In this study, we demonstrated that serum level malondialdehyde (MDA), an index lipid peroxidation, positively correlated with IL-13 negatively predicted forced expiratory...
Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on mainly focused extracting time-domain and frequency-domain features single electrodes, while paying little attention to the positional correlations between different EEG channels of same subject. Moreover, data imbalance is common scenarios where duration nonseizure periods much longer than seizures. To cope with two challenges, novel method based graph...
Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most mainly focus patient-specific scenarios while cross-patient is more challenging and meaningful. Regarding above problems, we propose a hybrid attention network (HAN) for automatic detection. Specifically, graph (GAT) extracts spatial features at front end,...
Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers semi-supervised hybrid architecture for detecting seizures, combines the extracted electroencephalogram (EEG) feature dataset convolutional autoencoder, called Fd-CAE. First, various features time domain entropy are to...
In software development, the raw requirements proposed by users are frequently incomplete, which impedes complete implementation of functionalities. With emergence large language models, exploration generating through user has attracted attention. Recent methods with top-down waterfall model employ a questioning approach for requirement completion, attempting to explore further requirements. However, users, constrained their domain knowledge, result in lack effective acceptance criteria...
Graphene oxide (GO) is widely used in biotechnology. The purpose of this study was to improve the efficiency genetic transformation by constructing a delivery system based on GO. First, GO applied traditional scheme for watermelons. We hydroponics and tissue culture methods determine optimal concentration watermelon plant growth, we then found that can inhibit growth Agrobacterium tumefaciens promote explants. This discovery simplify replacement various media after explant infection,...