Transactions on Neural Systems and Rehabilitation Engineering

Featured Articles
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper,... Read more
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Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data
Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely on simple classification methods, circumventing deep neural networks (DNNs) due... Read more
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Automated Diagnosis of COVID-19 using Deep Features and Parameter Free BAT Optimization
Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards Computed Tomography (CT) scans. Efforts are being made to design... Read more
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Detecting Cataract Using Smartphones
Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness.... Read more
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Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment
Goal: Smartphones can be used to passively assess and monitor patients’ speech impairments caused by ailments such as Parkinson’s disease, Traumatic Brain Injury (TBI), Post-Traumatic Stress Disorder (PTSD) and neurodegenerative diseases such as Alzheimer’s disease and dementia. However, passive audio... Read more
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Identifying hand use and hand roles after stroke using egocentric video
Objective: Upper limb (UL) impairment impacts quality of life, but is common after stroke. UL function evaluated in the clinic may not reflect use in activities of daily living (ADLs) after stroke, and current approaches for assessment at home rely... Read more
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Prediction of Freezing of Gait in Parkinson’s Disease Using Statistical Inference and Lower–Limb Acceleration Data
The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson’s disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home... Read more
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Deep Learning Classification of Systemic Sclerosis Skin using the MobileNetV2 Model
Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found... Read more
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MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then... Read more
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A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design... Read more