Papers
2
Total Citations
135
H-Index
2
About
Eric Yeung is a leading researcher at the intersection of neural engineering and soft robotics, with a primary focus on developing brain-computer interface (BCI)-controlled rehabilitation systems for post-stroke motor recovery. His most impactful work, the 2022 study on an SSVEP-based BCI-controlled soft robotic glove (101 citations), represents a significant advance in neural rehabilitation. Unlike conventional motor imagery (MI) systems, Yeung’s approach leverages steady-state visual evoked potentials (SSVEP) to achieve more reliable and faster BCI control, directly translating neural commands into precise actuation of a soft robotic glove for hand function restoration. This work addresses a critical gap in post-stroke therapy by providing a non-invasive, intuitive, and effective tool for patients with severe motor impairment. Complementing this, his 2020 simulation analysis of pneumatic bellow actuators (34 citations) established the foundational design principles for the glove’s soft actuation system, optimizing force output and compliance for safe, patient-specific use. Yeung’s contributions are notable for seamlessly integrating high-performance BCI decoding with adaptive soft robotics, demonstrating a clear pathway from laboratory innovation to clinical application. His research holds promise for transforming stroke rehabilitation by enabling more natural, volitional, and intensive hand function training.
Research Focus
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