Application of Higher Order Statistics to Surface Electromyogram Signal Classification

We propose a novel approach for surface electromyogram (sEMG) signal classification. This approach utilizes higher order statistics of sEMG signal to classify four primitive motions, i.e., elbow flexion, elbow extension, forearm supination, and forearm pronation. In documented research, the sEMG signal generated during isometric contraction is modeled by a stationary process whose probability density function (pdf) is assumed to be either Gaussian or Laplacian. In this paper, using Negentropy, we demonstrate that the level of non-Gaussianity of sEMG signal recorded in muscular forces below 25% of maximum voluntary contraction (MVC) is significant. Therefore, application of higher order statistics in sEMG signal processing is justified, due to the fact that more useful information can be extracted from the corresponding higher order statistics. An accurate classification is achieved by using the sequential forward selection (SFS) method for reducing of the dimensionality of feature space and the K-nearest neighbor (KNN) classifier. The results indicate that the proposed approach provides higher sEMG correct classification rates as compared to the existing methods.