Yisha Zhang

Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control

A novel sensory-guided joint learning framework that coordinates subject learning and decoder adaptation to improve BCI acquisition and advances BCI training from passive calibration to active human–machine joint learning, enabling practical and scalable neural interfaces for communication and rehabilitation.