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About

Faisal Mushtaq is a leading researcher at the intersection of human motor learning, neurorehabilitation, and surgical robotics. His work is unified by a central question: how can we understand and enhance human skill acquisition, particularly in high-stakes environments? He has made major contributions to brain-computer interface (BCI) robotics, demonstrated by his highly cited systematic review on BCI for hand rehabilitation after stroke (221 citations), which established a foundational framework for using EEG-based systems to restore motor function. Mushtaq’s research also explores the counterintuitive role of disturbance forces in motor learning, applying the Free Energy Principle to explain how perturbations can actually improve skill acquisition. He has advanced the field of surgical training by investigating laparoscopic motor learning and workspace exploration, and has contributed to the development of haptic feedback systems for surgical robots. His work on human-like planning for reaching in cluttered environments bridges robotics and cognitive science. With a portfolio spanning virtual reality for children to the neurophysiology of skill, Mushtaq’s research is shaping how we design rehabilitation technologies, train surgeons, and understand the fundamental principles of motor control.