This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface (BMI) and vision feedback. Vision compressive sensing, brainmachine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by video captured by a camera, allowing him/her to visualize the manipulator's workspace and the movements being executed. Then, compressed images are used as feedback errors in a nonvector space for producing SSVEP (Steady-State Visual Evoked Potentials) electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed following Lyapunov synthesis to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.

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