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IEEE Transactions on Neural Systems and Rehabilitation Engineering is a peer-reviewed journal published by IEEE, covering EEG and Brain-Computer Interfaces, Muscle activation and electromyography studies, Neuroscience and Neural Engineering. Over its lifetime it has published 5,264 papers, which have received 202,421 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Neural Systems and Rehabilitation Engineering style. No sign-up. |
| Publishes research in | EEG and Brain-Computer Interfaces Muscle activation and electromyography studies Neuroscience and Neural Engineering Stroke Rehabilitation and Recovery Prosthetics and Rehabilitation Robotics |
| ISSN | 1534-4320 |
| Citation impact (2-yr) | 5.49 |
| h-index | 190 |
| i10-index | 3,071 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Northwestern University |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next…
This paper introduces a newly developed gait rehabilitation device. The device, called LOPES, combines a freely translatable and 2-D-actuated pelvis segment with a leg exoskeleton containing three actuated rotational joints: two at the hip and one at the knee. The joints are impedance controlled to allow bidirectional mechanical interaction between the robot and the training…
Fuzzy entropy (FuzzyEn), a new measure of time series regularity, was proposed and applied to the characterization of surface electromyography (EMG) signals. Similar to the two existing related measures ApEn and SampEn, FuzzyEn is the negative natural logarithm of the conditional probability that two vectors similar for m points remain similar for the next m…
Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems,…
A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain…
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