A5006592210- EEG and Brain-Computer Interfaces
- Gaze Tracking and Assistive Technology
- Musculoskeletal pain and rehabilitation
- Pain Management and Treatment
- Artificial Intelligence in Healthcare
- Sleep and Wakefulness Research
- Vehicle License Plate Recognition
- Neuroscience and Neural Engineering
- ECG Monitoring and Analysis
- Hand Gesture Recognition Systems
- Brain Tumor Detection and Classification
- Advanced Neural Network Applications
- Smart Parking Systems Research
- Neural dynamics and brain function
- Pain Mechanisms and Treatments
Qilu University of Technology
2023-2025
Shandong Academy of Sciences
2023-2025
Traditional machine learning methods struggle with efficiency when processing large-scale data, while deep approaches, such as convolutional neural networks (CNN) and long short-term memory (LSTM), exhibit certain limitations handling long-duration sequences. The choice of kernel size needs to be determined after several experiments, LSTM has difficulty capturing effective information from long-time In this paper, we propose a transfer (TL) method based on Transformer, which constructs new...
Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the to construct models EEG data suffers from problem large amount required complexity algorithm.
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...
With the world moving towards low-carbon and environmentally friendly development, rapid growth of new-energy vehicles is evident. The utilization deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, energy-saving recognition due to their inherent limitations such as high latency energy consumption. An innovative Edge-LPR system that leverages edge computing lightweight network models...
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...