IEEE Transactions on Neural Systems and Rehabilitation Engineering

Aims & Scope

Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering, Medicine and Neuroscience, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science, Engineering, Medicine and Neuroscience, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 1,995.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation

  Citation: 1069

  Authors: Jan F., Rik, Edsko E. G., Ralf, Edwin H. F., Herman

• DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

  Citation: 1053

  Authors: Akara, Hao, Chao, Yike

• Characterization of Surface EMG Signal Based on Fuzzy Entropy

  Citation: 920

  Authors: Weiting, Zhizhong, Hongbo, Wangxin

• The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

  Citation: 808

  Authors: Dario, Ning, Hubertus, Ales, Bernhard, Hans, Oskar C.

• The BCI competition III: validating alternative approaches to actual BCI problems

  Citation: 779

  Authors: B., K.-R., D.J., G., J.R., A., G., Jd.R., M., N.

• State of the Art and Future Directions for Lower Limb Robotic Exoskeletons

  Citation: 730

  Authors: Aaron J., Daniel P.

• Robot Assisted Gait Training With Active Leg Exoskeleton (ALEX)

  Citation: 622

  Authors: Sai K., Seok Hun, Sunil K., John P.

• Patient-cooperative strategies for robot-aided treadmill training: first experimental results

  Citation: 590

  Authors: R., L., S., M., G., V.

• Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait

  Citation: 589

  Authors: J.A., H.

• Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

  Citation: 588

  Authors: Ulysse, Cheikh Latyr, Alexandre, Alexandre, Clement, Kyrre, Francois, Benoit