Real-Time Classification of Surface Electromyogram Signal using Correntropy

In this paper, we propose a novel approach for classification of surface electromyogram (sEMG) signal with a view to controlling myoelectric prosthetic devices. The sEMG signal generated during isometric contraction is modeled by a stochastic process whose probability density function (PDF) is non- Gaussian for low levels of applied force. Since the PDF of ambient noise is assumed to be Gaussian, we extract correntropy features, as they contain information on non-Gaussian components (the sEMG signal) only; and utilize the linear discriminant analysis (LDA) to classify the sEMG signal using correntropy features. Our proposed method has lower classification error and requires much less computations as compared to other existing advanced methods.