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About

Xinyi Yong is a neurotechnology researcher whose work sits at the intersection of brain-computer interfaces (BCIs), electroencephalography (EEG), and motor rehabilitation engineering. With a focused research agenda centered on decoding motor imagery signals from the brain, Yong has made significant contributions to one of the field's most persistent challenges: distinguishing imagined movements of different limb segments that share closely overlapping neural representations in the motor cortex. Her 2015 paper on EEG classification of imaginary movements within the same limb has garnered 134 citations, establishing her as a notable voice in BCI signal processing. Building on this foundation, she has advanced multi-class classification frameworks — including a 2017 study achieving three-state discrimination of upper extremity imagery — and has explored practical applications through affordable BCI-controlled arm exoskeletons aimed at stroke rehabilitation. This translational thread in her research is particularly compelling, addressing real-world barriers such as cost and accessibility in post-stroke recovery. Collectively, Yong's body of work bridges fundamental neural decoding science with clinically meaningful assistive technologies, offering meaningful pathways toward greater independence for individuals living with upper extremity impairments.