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In recent decades, biomedical signals have been used for communication inHuman-Computer Interfaces (HCI) for medical applications; an instance of thesesignals are the myoelectric signals (MES), which are generated in the musclesof the human body as unidimensional patterns. Because of this, the methods andalgorithms developed for pattern recognition in signals can be applied fortheir analyses once these signals have been sampled and turned intoelectromyographic (EMG) signals. Additionally, in recent years, manyresearchers have dedicated their efforts to studying prosthetic controlutilizing EMG signal classification, that is, by logging a set of MES in aproper range of frequencies to classify the corresponding EMG signals. Thefeature classification can be carried out on the time domain or by using otherdomains such as the frequency domain (also known as the spectral domain), timescale, and time-frequency, amongst others. One of the main methods used forpattern recognition in myoelectric signals is the Support Vector Machines (SVM)technique whose primary function is to identify an n-dimensional hyperplane toseparate a set of input feature points into different classes. This techniquehas the potential to recognize complex patterns and on several occasions, ithas proven its worth when compared to other classifiers such as ArtificialNeural Network (ANN), Linear Discriminant Analysis (LDA), and PrincipalComponent Analysis(PCA). The key concepts underlying the SVM are (a) thehyperplane separator; (b) the kernel function; (c) the optimal separationhyperplane; and (d) a soft margin (hyperplane tolerance).

SVM and ANN based Classification of EMG signals by using PCA and LDA