Upper Limb Prosthesis Control: A Hybrid EEG-EMG Scheme for Motion Estimation in Transhumeral Subjects

This study described the use of Kernel Least Square Tracker based estimation for 3-dimensional shoulder, elbow motion kinematics from surface Electromyogram (EMG) and a two-stage multiclass Support Vector Machine based classification of different wrist, grip and finger motions from Electroencephalogram (EEG). The advantage of employing hybrid EEG-EMG strategy for upper limb motion estimation was demonstrated for a transhumeral subject. The method utilized EMG from upper arm muscles for elbow motion (and shoulder motion in case of higher degree amputation scenario) and used EEG for discerning basic wrist, grip and finger motions. The results showed that the hybrid scheme could estimate shoulder, elbow motion with more than 90% accuracy and wrist, grip and finger motion with 65%-70% accuracy. This strategy of using hybrid EEG-EMG motion estimation, thus, could be employed in developing a more intuitive upper limb prosthesis controller with multiple degrees of freedom.