Papers
2
Total Citations
8
H-Index
2
About
Huijuan Yang is a researcher focused on advancing brain-machine interfaces (BMI) and functional neuroimaging. Her work addresses the critical challenge of neural signal nonstationarity—the day-to-day variability in signal quality and tuning properties that hinders reliable BMI performance. In her most-cited work (2017, 5 citations), Yang pioneered a classifier-level fusion approach using accumulative training models from multi-day data, demonstrating that rich temporal dynamics can be harnessed to boost decoding stability and accuracy. This contribution offers a practical pathway toward more robust, real-world BMI systems. Yang has also explored cortical activation patterns using functional Near-Infrared Spectroscopy (fNIRS), investigating passive hand movement with a Haptic Knob (2014, 3 citations). This preliminary study provided early insights into sensorimotor function during passive motion, an area with limited prior fNIRS research. By bridging multi-day learning strategies and non-invasive neuroimaging, Yang’s work contributes foundational knowledge for adaptive neuroprosthetics and rehabilitation technologies. Her research continues to inform the development of more resilient, user-adaptive brain-machine interfaces.
Research Focus
Key Achievements
Top Papers
- 1
- 2