A5008600851- EEG and Brain-Computer Interfaces
- Endometriosis Research and Treatment
- Gynecological conditions and treatments
- Uterine Myomas and Treatments
- Functional Brain Connectivity Studies
- Minimally Invasive Surgical Techniques
- Artificial Intelligence in Healthcare
- Neural dynamics and brain function
- Brain Tumor Detection and Classification
- Advanced Memory and Neural Computing
- Musculoskeletal pain and rehabilitation
- Bariatric Surgery and Outcomes
- Hernia repair and management
- Sleep and Wakefulness Research
- ECG Monitoring and Analysis
- Pain Mechanisms and Treatments
- Neuroscience and Neural Engineering
- Pain Management and Treatment
- Heart Rate Variability and Autonomic Control
Shandong Academy of Sciences
2023-2024
Qilu University of Technology
2023-2024
First Hospital of Shijiazhuang
2015-2016
Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals patients is challenging because their low signal-to-noise ratio and high variability. Therefore, we propose novel approach that combines modified S-transform (MST) dense graph convolutional network (DenseGCN) algorithm to enhance MI-BCI performance across time, frequency,...
The aim of this study was to compare the clinical results total laparoscopic hysterectomy (TLH) for large uterus with size 12 gestational weeks (g.w.) or greater through transvaginal uterine morcellation approaches. We retrospectively collected data those undergoing hysterectomies between January 2004 and June 2012. Intraoperative postoperative outcomes were compared patients whose removed group has significantly shorter mean operation time removal smaller incidence intraoperative...
Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker CNP. To analyze changes in brain network mechanism under combined effect and or pain, this paper mainly studies functional connectivity patients with without SCI. This has EEG CNP group SCI, healthy control group. Phase-locking value been construct topological maps. By comparing networks two groups...
Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition physical limitations of subjects. Therefore, how learn effective feature representation is very important. Deep learning networks been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework modified s-transform (MST) generate MST-CPC representations. MST acquire temporal-frequency...
Background As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due their complexity, weakness, differences between subjects. At present, most of the current research on sleep single-channel dual-channel, ignoring relationship different brain regions. Brain functional connectivity is considered be closely related activity can used study interaction areas. Methods Phase-locked value (PLV) construct connection network. The network...
Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions people are affected by pain disorders. There particular challenges in measurement assessment pain, commonly used measuring tools include traditional subjective scoring methods biomarker-based measures. The main for analysis electroencephalography (EEG), electrocardiography functional magnetic resonance. EEG-based quantitative measurements immense...
Abstract Electroencephalogram (EEG) signals exhibit multi-domain features, and electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a Temporal-Frequency-Spatial feature fusion Graph Attention Network (TFSGAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients. The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between channels continuous wavelet transform...