Wheelchair control using an EOG- and EMG-based gesture interface

In our previous study, we presented a nonverbal interface that used biopotential signals, such as electrooculargraphic (EOG) and electromyographic (EMG), captured by a simple brain-computer interface. In this paper, we apply the nonverbal interface to hands-free control of an electric wheelchair. Based on the biopotential signals, the interface recognizes the operator's gestures, such as closing the jaw, wrinkling the forehead, and looking towards left and right. By combining these gestures, the operator controls linear and turning motions, velocity, and the steering angle of the wheelchair. Experimental results for navigating the wheelchair in a hallway environment confirmed the feasibility of the proposed method.