Estimation of forearm supination/pronation motion based on EEG signals to control an artificial arm

In recent years, many myoelectric arms that are controlled based on electromyogram (EMG) signals of amputee's stump or residual muscles have been proposed. In the cases of above elbow amputees, however, the muscles which generate the forearm, wrist and hand motions do not remain. Therefore, most myoelectric arms for above elbow amputees have less degree of freedom and its dexterity is relatively poor compared with a human upper-limb. To improve the quality of life of above elbow amputees and to increase their mobility in daily life activities, some additional input signals must be prepared. One of the strong candidates of the additional input signals is an electroencephalogram (EEG) signal. An EEG signal is an electric signal that can be measured along a scalp, so that it can be measured even with an above elbow amputee. In this study, an artificial arm for above elbow amputees is controlled based on EMG and EEG signals. In this paper, the EEG-based motion estimation method is proposed to control the forearm supination/pronation motion of the artificial arm. The angle, angular velocity, and angular acceleration of the forearm motion are estimated under several velocities by using EEG signals.