Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and
intervention of sleep disorders and related diseases. Although previous attempts to classify …

The electroencephalogram (EEG) is extensively employed for detecting various brain
electrical activities. Nonetheless, EEG recordings are susceptible to undesirable artifacts …

Motor imagery (MI) has emerged as a classical paradigm in brain-computer interface (BCI)
research. In recent years, advancements in deep learning techniques, such as the …

With the development of time, traditional teaching methods cannot meet the needs of
education and society for innovative talents. Blended learning conforms to the reform of …

Neural ordinary differential equation (Neural ODE) is an elegant yet powerful framework to
learn the temporal dynamics for time series modeling. However, we observe that existing …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

Automatic sleep staging is important for improving diagnosis and treatment, and machine
learning with neuroscience explainability of sleep staging is shown to be a suitable method …

Cardiovascular diseases (CVDs) are the leading global cause of death. As first-line
diagnostic tool for CVDs, electrocardiograms (ECG) non-invasively assess cardiac condition …

Graph neural networks have been successfully applied to sleep stage classification, but
there are still challenges:(1) How to effectively utilize epoch information of EEG-adjacent …

Good learning adaptability is the key to ensure students' learning quality. Learning
maladjustment not only affects students' learning effect but also affects the effectiveness of AI …