Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multi-User Myoelectric Interface.

IEEE transactions on bio-medical engineering

PubMedID: 23475334

Matsubara T, Morimoto J. Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multi-User Myoelectric Interface. IEEE Trans Biomed Eng. 2013;.
In this study, we propose a multi-user myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different EMG signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user-dependent and 2) motion-dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multi-user interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard non-multi user interfaces, as the result of a two-sample t-test at a significance level of 1%.