Deep-Learning-Based Motor Imagery EEG Classification by Exploiting the Functional Connectivity of Cortical Source Imaging

Information Science, Electronic Information, Automation, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
deep learning   motor imagery EEG   cortical source imaging   brain functional connectivity   spatiotemporal features   graph convolutional network  

Deep-learning-based Motor Imagery EEG Classification by Exploiting the Functional Connectivity of Cortical Source Imaging Research Background and Motivation A brain-computer interface (BCI) is a system that directly decodes and outputs brain activity information without relying on related neural pathways and muscles, thereby achieving communication...

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, ...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Engineering, Electrical Engineering, Information Science, Automation, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
multi-feature attention   convolutional neural networks   motor imagery   brain-computer interface   lightweight model  

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...

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

Engineering, Electrical Engineering, Information Science, Automation, Medicine, Basic Medical Sciences, Neurology, Medical Imaging, Life Sciences, Bioengineering,
EMG-driven   robot hand training   stroke rehabilitation   neural networks   dynamic causal modeling   upper limb function recovery  

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...