Papers
3
Total Citations
107
H-Index
3
About
Yijun Zou is a leading researcher in brain-computer interfaces (BCIs) and neural signal processing, with a focus on decoding motor intent for rehabilitation. Her work addresses the critical challenge of translating electroencephalogram (EEG) signals—notably motor imagery (MI) and steady-state visual evoked potentials (SSVEP)—into precise commands for assistive robotics. In her most cited paper (89 citations), Zou pioneered a method combining Riemannian geometry features with partial least squares regression to decode multiclass MI EEG from the same upper limb, overcoming the low spatial resolution and poor signal-to-noise ratio that typically hinder high-accuracy classification. She further advanced the field by developing a robot-assisted rehabilitation system driven by SSVEP-based BCIs, enabling tetraplegia patients to control upper extremity devices without relying on peripheral nerves. Her comparative study of feature extraction methods for MI EEG decoding within the same limb provides a foundational benchmark for the field. Through these contributions, Zou has significantly improved the practicality and precision of non-invasive BCIs, directly impacting the design of more intuitive, patient-responsive neurorehabilitation technologies.
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