Abstract
The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions.
Highlights
Electromyography (EMG) is the electrical activity of muscle cells and has been used for the classification of actions [1,2,3,4,5,6,7], disease detection [8], prosthetic hand control [9], and emotion detection [10]
The extreme learning machine (ELM) was used in order to classify the EMG signals as either belonging to an aggressive action or a normal action
In the 1st stage of the quadratic phase coupling (QPC), each 10 s EMG episode was determined by bispectral analysis
Summary
Electromyography (EMG) is the electrical activity of muscle cells and has been used for the classification of actions [1,2,3,4,5,6,7], disease detection [8], prosthetic hand control [9], and emotion detection [10]. 8 channels recorded the EMG signals of 10 aggressive and 10 normal actions of 3 males and 1 female, which were analyzed for classifying normal and aggressive actions. With this purpose in mind, a composed model of higher-order spectra (HOS) and the learning machine algorithm was proposed. Many signals have nonlinearity and non-Gaussian behavior, and such signals cannot be examined properly by 2nd-order statistical methods. Since EMG signals are nonstationary and non-Gaussian signals, they should be examined by HOS methods. Bispectrum, which is the Fourier transform of the 3rd-order cumulant, can be applied to nonlinear and non-Gaussian signals to extract nonlinear information
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