Minghua Jiang
Papers
1
Total Citations
9
H-Index
1
About
Minghua Jiang is a leading researcher at the intersection of brain-computer interfaces (BCIs) and assistive robotics, with a focus on restoring motor function for individuals with physical disabilities. His most cited work, "ArmBCIsys: Robot Arm BCI System With Time–Frequency Network for Multiobject Grasping" (2025, 9 citations), introduces a novel system that enables precise, multiobject grasping through low-signal EEG decoding. By integrating time–frequency neural networks, Jiang’s system overcomes key limitations in BCI robustness and real-time control, allowing users to command robotic arms with greater accuracy and fewer calibration demands. This contribution addresses a critical gap in assistive technology, moving BCIs from single-action commands to complex, multi-step object manipulation. Jiang’s research has immediate implications for neuroprosthetics and human–robot interaction, offering a scalable framework for future clinical applications. His work is widely cited in BCI and rehabilitation engineering communities, reflecting its impact on both algorithm design and practical system deployment. Through ArmBCIsys, Jiang demonstrates how advanced signal processing can transform low-fidelity neural data into high-precision motor control, paving the way for more intuitive and capable assistive devices.
Research Focus
Key Achievements
Top Papers
- 1