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

Abstract

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (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 multiuser 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 nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

A Survey of Brain-Computer-Interaction Methods and Algorithms

“…The EEG signals were not used to control the mouse movement, but to determine the subject's control state. Takamitsu Matsubara [2], proposed a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions different electromyography (EMG) signals are measured.…”

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

“…EMG is one kind of signal that can be utilized as a control signal from the artificial hand that will be developed [1,2]. Widely used classification methods that can be used to obtain high classification accuracy are artificial neural network [3,4], fuzzy classifiers, neuro-fuzzy classifiers [5] and the other probabilistic based methods [6].…”

Hand Motion Recognition from Single Channel Surface EMG Using Wavelet & Artificial Neural Network

“…Classification of hand movements by the help of sEMG signals find wide range of application such as controlling of prosthetic hands, human computer interface(HCI) and rehabilitation robots etc 4,5,6 . In order to get high classification rates there are several methods and algorithms have been developed such as ANNs 3 , fuzzy classifiers, neuro-fuzzy classifiers 4 and other probabilistic based methods 5 .…”