This application discloses a method and
system for controlling a
lower limb exoskeleton using a brain-computer interface based on intent confidence, relating to the field of
medical rehabilitation technology. First, the
system identifies the original electroencephalogram (EEG)
signal to obtain intent confidence, and maps an impedance parameter set including
joint stiffness and damping coefficients by combining
gait phase information. Second, it extracts motion position and interaction force errors, compares the intent confidence with the actual execution state using a
sliding time window to obtain neural matching errors, and merges these three into a total composite error. Simultaneously, it assesses human-
machine coupling compliance based on changes in human joint angles and
exoskeleton torque, and generates a safety
gain coefficient. Finally, it performs
gain calculations on the total composite error based on the impedance parameter set, and corrects it with the safety
gain coefficient to obtain the
target control torque for output to the
actuator. This constructs a neural-force-motor three-loop architecture, achieving compliant and adaptive on-demand assistance and highly safe human-
machine collaborative
rehabilitation with defensive protection.