A Wearable Fluorescence Imaging Device for Intraoperative Identification of Human Brain Tumors

Malignant Glioma (MG) Report Malignant Glioma (MG) is the most common type of primary malignant brain tumor. Surgical resection of MG remains the cornerstone of treatment, and the extent of resection is highly correlated with patient survival. However, it is difficult to distinguish tumor tissue from normal tissue during surgery, which greatly limi...

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

In modern medical diagnostics and clinical research, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technology provides significant information regarding tissue pathophysiology. By fitting a Tracer-Kinetic (TK) model, pharmacokinetic (PK) parameters can be extracted from time-series MRI signals. However, these estimated PK parameter...

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Musculoskeletal models have been widely used in biomechanical analysis because they can estimate motion variables that are difficult to measure directly in living organisms, such as muscle forces and joint moments. Traditional physics-driven computational musculoskeletal models can explain the dynamic interactions between neural inputs to muscles, ...

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...

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...

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...

Wavelet-Based Temporal-Spectral-Attention Correlation Coefficient for Motor Imagery EEG Classification

Brain-Computer Interface (BCI) technology has rapidly developed in recent years and is considered a cutting-edge technology that allows external devices to be controlled directly by the brain without the need for peripheral nerves and muscles. Particularly in the application of Motor Imagery Electroencephalography (MI-EEG), BCI technology has shown...

An Attention-Based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG

The IEEE “Transactions on Neural Systems and Rehabilitation Engineering” published a paper titled “Sleep Stage Classification Using Attention-Based Deep Learning for Single-Channel EEG” in Volume 29, 2021. The author of the article include Emadeldeen Edele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. The main go...

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System

Measurement of Human Auditory Evoked Fields Using a Flexible Multichannel Optically Pumped Magnetometer MEG System Xin Zhang and others, from the Suzhou Institute of Biomedical Engineering and Technology of the Chinese Academy of Sciences, University of Science and Technology of China, Jihua Laboratory in Foshan, Guangdong Province, and Guoke Medic...