Jingyuan Bai
Papers
1
Total Citations
4
H-Index
1
About
Jingyuan Bai is a researcher whose work centers on biomedical signal processing, with a particular focus on high-density surface electromyography (HD-sEMG). Their major contribution lies in developing advanced noise-removal techniques that are both noise-specific and adaptive, addressing a critical challenge in the field: the contamination of HD-sEMG recordings by multiple interference sources, including power line interference, white Gaussian noise, baseline wandering, and motion artifacts. Bai’s 2024 paper, “A VMD-Based Noise-Specific and Adaptive Removal Method for High-Density Surface EMG,” introduces a variational mode decomposition (VMD) approach that selectively targets and suppresses each noise type without distorting the underlying motor unit activity. Although recently published, this work has already garnered 4 citations, signaling its relevance to researchers striving for cleaner neural and muscular signal acquisition. By enabling more accurate detection of motor unit activity, Bai’s methodology holds promise for advancing prosthetics, neuromuscular diagnostics, and human-machine interfaces. Their innovative, problem-driven approach marks them as a rising contributor to the intersection of signal processing and biomedical engineering.
Research Focus
Key Achievements
Top Papers
- 1