Pengfei Song
Papers
1
Total Citations
32
H-Index
1
About
Pengfei Song is an emerging researcher at the intersection of neurorehabilitation engineering and brain-computer interface technologies, with a particular focus on stroke recovery and motor function restoration. His most recognized work investigates the efficacy of bilateral upper limb robot-assisted rehabilitation in stroke patients, employing quantitative electroencephalography (EEG) as an objective neurophysiological measure to evaluate treatment outcomes. This innovative approach bridges the gap between robotic rehabilitation engineering and neuroscience, offering rigorous, data-driven evidence for clinical practice. The study has accumulated 32 citations since its 2023 publication, a strong indicator of early impact in a competitive and rapidly evolving field. By combining advanced robotics with quantitative brain signal analysis, Song's research contributes meaningfully to understanding how bilateral motor training drives neuroplastic changes in post-stroke populations — a question of profound clinical significance given the global burden of stroke-related disability. His work appeals to clinicians, biomedical engineers, and neuroscientists alike, positioning him as a promising contributor to the future of intelligent, evidence-based neurorehabilitation strategies for upper limb motor recovery.
Research Focus
Key Achievements
Top Papers
- 1