Ying Song
Papers
1
Total Citations
20
H-Index
1
About
Ying Song is a leading researcher in neurorehabilitation engineering, with a focus on understanding how robotic and assistive technologies reshape brain function after stroke. Her most-cited work, “fNIRS-based brain functional response to robot-assisted training for upper-limb in stroke patients with hemiplegia” (2022, 20 citations), provides critical insights into the cortical mechanisms underlying robot-assisted therapy (RAT). By using functional near-infrared spectroscopy (fNIRS), Song demonstrated how RAT elicits specific brain functional responses in hemiplegic patients, offering a theoretical foundation for tailoring rehabilitation protocols to individual neural recovery patterns. This research bridges the gap between engineering and clinical neuroscience, directly informing the design of more effective, patient-specific robotic interventions. Her contributions have been widely recognized for advancing personalized stroke rehabilitation, and her work continues to shape how clinicians and engineers approach motor recovery. With a growing citation impact, Song’s studies are essential reading for anyone interested in the intersection of robotics, brain imaging, and neuroplasticity.
Research Focus
Key Achievements
Top Papers
- 1