DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

In recent years, Virtual Reality (VR) technology has become increasingly common, with related hardware becoming more affordable. For example, current head-mounted displays (HMDs) on the market not only offer high-resolution displays but also feature precise head and handheld controller tracking. Initially, these technologies were mostly used in the...

In recent years, due to the high incidence and lethality of brain tumors, rapid and accurate detection and classification of brain tumors have become particularly important. Brain tumors include both malignant and non-malignant types, and their abnormal growth can cause long-term damage to the brain. Magnetic Resonance Imaging (MRI) is a commonly u...

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. However, existing classification methods still face numerous challenges in handling the spatial and temporal features of time-varying multi-channel brain signals, coping with individual differences in biological signals, and model interpretability. Traditional...

Background Introduction Motor Imagery (MI) refers to performing activities through imagination without actual muscle movement. This paradigm is widely used in Brain-Computer Interface (BCI) to decode brain activities into control commands for external devices. Specifically, Electroencephalography (EEG) is widely used in BCI due to its relative affo...

Research Review on the Predictive Value of Low-Grade Glioma Postoperative Growth Models Introduction Glioma is an aggressive brain tumor whose cells rapidly diffuse within the brain. Understanding and predicting the pattern and speed of this diffusion can help optimize treatment plans. Glioma growth models based on diffusion-proliferation have show...

MI-EEG Decoding Using a Temporal Dependency Learning Convolutional Neural Network (CNN) Based on Attention Mechanism Research Background and Problem Description Brain-Computer Interface (BCI) systems provide a new way of communicating with computers by real-time translation of brain signals. In recent years, BCI technology has played an important r...

In the current educational environment, understanding students’ learning styles is crucial for improving their learning efficiency. Specifically, the identification of visual learning styles can help teachers and students adopt more effective strategies in the teaching and learning process. Currently, automatic identification of visual learning sty...

Spinal Cord Injury (SCI) resulting in upper limb paralysis significantly impacts patients’ independence and quality of life. Among the SCI patient population, regaining control over hand and arm movements is considered the highest priority treatment goal, surpassing even the restoration of walking ability. However, current clinical methods to impro...

Brain-Computer Interface (BCI) is a communication method that connects the nervous system to the external environment. Motor Imagery (MI) is the cornerstone of BCI research, referring to the internal rehearsal before physical execution. Non-invasive techniques such as Electroencephalography (EEG) can record neural activities with high temporal reso...