A5006417050- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Infrastructure Maintenance and Monitoring
- Blind Source Separation Techniques
- ECG Monitoring and Analysis
- Concrete Corrosion and Durability
- Structural Health Monitoring Techniques
Fudan University
2023
Guangzhou University
2023
State Key Laboratory of ASIC and System
2023
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop lightweighted deep learning model while retaining high level of classification accuracy. To do so, weighted neighborhood field (WNFG) represent EEG signals. WNFG reduces redundant edges between nodes and has lower generation time memory usage than the baseline solution. sequential further developed from by combining weight...
Epilepsy is one of the most common neurological diseases. Clinically, epileptic seizure detection usually performed by analyzing electroencephalography (EEG) signals. At present, deep learning models have been widely used for single-channel EEG signal epilepsy detection, but this method difficult to explain classification results. Researchers attempted solve interpretive problems combining graph representation signals with neural network models. Recently, combination <italic...