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

Engineering, Electrical Engineering, Medicine, Neurology, Neurosurgery, Medical Imaging, Life Sciences, Bioengineering,
Intraoperative imaging device   Malignant glioma   Brain tumors   Fluorescence-guided surgery   Wearable device  

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...

An Explicit Estimated Baseline Model for Robust Estimation of Fluorophores Using Multiple-Wavelength Excitation Fluorescence Spectroscopy

Physics, Optics, Chemistry, Analytical Chemistry, Medicine, Medical Laboratory Science, Neurology, Neurosurgery, Medical Imaging, Oncology, Life Sciences, Bioengineering,
fluorescence spectroscopy   multiple-wavelength excitation   fluorophores   baseline model   non-linear optics   neuro-oncology   glioma  

Research Background Fluorescence spectroscopy is a widely used method for identifying and quantifying fluorescent substances (fluorophores). However, quantifying the fluorophores of interest becomes challenging when the material contains other fluorophores (baseline fluorophores), especially when the emission spectrum of the baseline is not well-de...

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

Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
sleep stage classification   spatial-temporal graph convolution   transfer learning   domain generalization   brain networks  

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...

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

Information Science, Automation, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Temporal Dependency Learning   Convolutional Neural Networks   Brain-Computer Interface   Motor Imagery   Attention Mechanism  

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...

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

Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence, Medicine, Orthopedics, Medical Laboratory Science, Medical Imaging, Life Sciences, Bioengineering,
physics-informed   deep learning   musculoskeletal modeling   EMG   muscle forces   joint kinematics  

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, ...