Multi-view Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

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...

Multi-task Heterogeneous Ensemble Learning-based Cross-subject EEG Classification in Stroke Patients

Multi-task Heterogeneous Ensemble Learning-based Cross-subject EEG Classification in Stroke Patients

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...

Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma after Tumor Resection

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...

A Temporal Dependency Learning CNN with Attention Mechanism for MI-EEG Decoding

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...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG

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...

Transcutaneous Spinal Cord Stimulation Restores Hand and Arm Function After Spinal Cord Injury

Transcutaneous Spinal Cord Stimulation Restores Hand and Arm Function After Spinal Cord Injury

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...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

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...

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

Research Background Brain-Computer Interface (BCI) technology enables direct communication between the brain and external devices. It is widely used in fields such as human-computer interaction, motor rehabilitation, and healthcare. Common BCI paradigms include steady-state visual evoked potentials (SSVEP), P300, and motor imagery (MI). Among these...

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

Research Background and Objectives In recent years, Brain-Computer Interface (BCI) systems have been widely applied in the fields of neuroengineering and neuroscience. Electroencephalogram (EEG), as a data tool reflecting the activities of different neuronal groups in the central nervous system, has become a core research topic in these fields. How...

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke through EMG-Driven Robot Hand Training: Insights from Dynamic Causal Modeling

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke through EMG-Driven Robot Hand Training: Insights from Dynamic Causal Modeling

Revealing the Neuromechanism of Interhemispheric Balance Restoration in Chronic Stroke Patients through EMG-driven Robot Hand Training: Insights from Dynamic Causal Modeling Stroke is a common cause of disability, with most stroke survivors suffering from upper limb paralysis. The consequences of upper limb functional impairment can persist for ove...