TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.
Chenyun Dai received the B.S. degree in electrical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, and the M.S. degree in electrical engineering from Worcester Polytechnic Institute, Worcester, MA, USA, where is currently working toward the Ph.D. degree in the Department of Electrical and Computer Engineering. His research interests include biomedical signal processing, system identification, and modeling.
TNSRE, Featured ArticlesCross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data
Abstract The reliability of clinical and scientific information provided by algorithms that automatically decompose the electromyogram (EMG) depends on the algorithms’ accuracies. We used experimental and simulated data to assess the agreement and accuracy of three publicly available decomposition algorithms—EMGlab (McGill... Read more
Posted on 6 JAN 2015
TNSRE, Featured ArticlesOpen Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings
Xinyu Jiang, Xiangyu Liu, Jiahao Fan, Xinming Ye, Chenyun Dai, Edward A. Clancy, Metin Akay, Wei Chen
We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named “Hyser”), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on... Read more
Posted on 4 JUN 2021