deep learning

A Functional Connectivity-Based Model with a Lightweight Attention Mechanism for Depression Recognition Using EEG signals
A Functional Connectivity-Based Model with a Lightweight Attention Mechanism for Depression Recognition Using EEG signals 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Numerous studies on depression recognition utilize attention mechanisms as tools for feature extraction. Applying the standard multi-head self-attention mechanism to the spatial domain of EEG data is a feasible approach… read more

Long-Term Gait-Balance Monitoring Artificial Intelligence System for Various Terrain Types
Long-Term Gait-Balance Monitoring Artificial Intelligence System for Various Terrain Types 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
A long-term gait-balance monitoring system for various terrain types was developed using an inertial measurement unit (IMU) and deep-learning model. The system aims to identify unstable gait caused by lower-limb… read more

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging
AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep… read more

A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition
A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Effectively integrating the time-space-frequency information of multi-modal signals from armband sensor, including surface electromyogram (sEMG) and accelerometer data, is critical for accurate gesture recognition. Existing approaches often neglect the abundant… read more

Graph Neural Network-Based EEG Classification: A Survey
Graph Neural Network-Based EEG Classification: A Survey 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been…

read more

Automated Hand Prehension Assessment From Egocentric Video After Spinal Cord Injury
Automated Hand Prehension Assessment From Egocentric Video After Spinal Cord Injury 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Hand function assessments in a clinical setting are critical for upper limb rehabilitation after spinal cord injury (SCI) but may not accurately reflect performance in an individual’s home environment. When…

read more

UCLN: Toward the Causal Understanding of Brain Disorders With Temporal Lag Dynamics
UCLN: Toward the Causal Understanding of Brain Disorders With Temporal Lag Dynamics 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool for exploring interactions among brain regions. A growing body of research is actively investigating various computational approaches for… read more

Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings
Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early… read more

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions
MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal… read more

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data
Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting… read more
Search this website