Transactions on Neural Systems and Rehabilitation Engineering

Featured Articles
VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel
Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield... Read more
Articles
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
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Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke
This paper presents the results of a study involving the use of a Brain-Computer Interface-based Soft Robotic Glove as a novel strategy in stroke rehabilitation. The technology uses the electroencephalogram signals from stroke patients to drive the assistive actions of the soft robotic glove to assist them in physically carrying out activities of daily living. The two-arm study showed prolonged improvements in FMA and ARAT scores although no significant intergroup differences were observed during the study. In addition, all of the patients in the BCI-SRG group also experienced a vivid kinesthetic illusion lasting beyond the active intervention period... Read more
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Dynamic reorganization of functional connectivity unmasks fatigue related performance declines in simulated driving
Although driving fatigue has long been recognized as one of the leading causes of fatal accidents worldwide, the underlying neural mechanisms remain largely unknown that impedes the developments of automatic detection techniques... Read more
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Small-World Propensity Reveals the Frequency Specificity of Resting State Networks
Functional connectivity (FC) is an important indicator of the brain’s state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed... Read more
Articles, Published Articles
Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals
   Early Access Note: Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are... Read more
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Electrophysiological Brain Connectivity: Theory and Implementation
Brain function and dysfunction are encoded in networks within the brain that are distributed over 3-dimensional space and evolves in time. It is of great importance to image brain activation and functional connectivity which are the building blocks of neural... Read more
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An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences
     Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many... Read more
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Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?
EEG-based brain-computer interface (BCI) has been used to detect movement intention in severely affected stroke patients during assisted therapy, but current EEG-BCI systems are not practical for routine clinical use. If detectable in this population, residual EMG in the target... Read more
Featured Articles
Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer
Adaptive beamformer methods have been used extensively for functional brain imaging using EEG/MEG surface recordings. However, the sensitivity of beamformers to model mismatches impedes their widespread application, in practice. In this study, we propose a state-of-the-art technique, termed robust minimum... Read more