Transactions on
Neural Systems and Rehabilitation Engineering

IEEE Transactions on Neural Systems and Rehabilitation Engineering focuses on the rehabilitative and neural aspects of biomedical engineering.
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Daniel P. Ferris, Ph.D.
Editor-in-chief
Editor-in-chief

Daniel P. Ferris is the Robert W. Adenbaum Professor of Engineering Innovation at the University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering. He studies how to integrate machines and humans to improve human performance and mobility in health and disability. Specific research projects focus on robotic lower limb exoskeletons, bionic lower limb prostheses, and mobile brain imaging with high-density electroencephalography. Prof. Ferris completed his B.S. from the University of Central Florida, his M.S. from the University of Miami, and his Ph.D. from University of California, Berkeley. After earning his doctoral degree, he worked as a post-doctoral researcher in the UCLA Department of Neurology and the University of Washington Department of Electrical Engineering.​ Dr. Ferris spent 16 years at the University of Michigan until recently relocating to the University of Florida in June 2017.​

Daniel P. Ferris is the Robert W. Adenbaum Professor of Engineering Innovation at the University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering. He studies how to integrate machines and humans to improve human performance and mobility in health and disability. Specific research projects focus on robotic lower limb exoskeletons, bionic lower limb prostheses, and mobile brain imaging with high-density electroencephalography. Prof. Ferris completed his B.S. from the University of Central Florida, his M.S. from the University of Miami, and his Ph.D. from University of California, Berkeley. After earning his doctoral degree, he worked as a post-doctoral researcher in the UCLA Department of Neurology and the University of Washington Department of Electrical Engineering.​ Dr. Ferris spent 16 years at the University of Michigan until recently relocating to the University of Florida in June 2017.​

Updates

Transactions on

Neural Systems and Rehabilitation Engineering

JULY 2020
VOLUME 28
NUMBER 7
ITNSB3
28
The IEEE Transactions on Neural Systems and Rehabilitation Engineering Volume 28 Issue 5 has been published.
Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review
Benchmarks have long been used to verify and compare the readiness level of different technologies in many application domains. In the field of wearable robots, the lack of a recognized benchmarking methodology is one important impediment that may hamper the efficient translation of research prototypes into actual products... Read more
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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Learning, Generalization, and Scalability of Abstract Myoelectric Control
Motor learning-based methods offer an alternative paradigm to machine learning-based methods for controlling upper-limb prosthetics. Within this paradigm, the patterns of muscular activity used for control can differ from those which control biological limbs... Read more
Featured Articles
Evaluating Auditory Neural Activities and Information Transfer Using Phase and Spike Train Correlation Algorithms
The coherence of neural activities among different areas in the brain has received great attention because it is valuable in understanding the functional mechanism of brain structures. While many methodologies, such as time-frequency and entropy analysis, have been applied to evaluate relations between neural signals, these techniques haven’t been effective in assessing neural communication in order to reach conclusions... Read more
Featured Articles
Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control
Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution... Read more