# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification
### SVM-Based Myoelectric Signal Classification
Another method that is used to reduce the dimensionality problem is that used by [PERSON] and [PERSON] [48], which, in addition to applying FFT to four MES signals for the camera control, whose movements are up, downright, left and neutral, performs a component reduction by SPCA (Sparse Principal Component Analysis), before entering SVM values. The authors implemented a not widely used technique, namely the data division in Kaiser windows, obtaining 81% accuracy. In the same manner, [PERSON] and [PERSON] [5] employed SPCA with the lasso to produce modified principal components with scattered loads. In addition, they used the SVM ensemble for classification of seven different arm movements, combined with a window length of 256 ms and a 50% overlapping. The classification accuracy obtained by the authors reached 98%. Unlike other authors, [PERSON] et al. [62] simulated, in the laboratory of the University of Waterloo, MES signals by incorporating statistical and morphological properties. They utilized SVM to discriminate between different neuromuscular diseases (neuropathy and myopathy), and, after simulating signals, decomposed them by using an Empirical Mode Decomposition (EMD) as well as the Kolmogorov complexity and other informative features to reveal the number of irregularities within each subspace formed by EMD. The accuracy obtained was 91.11%. Combining time and frequency domain features, [PERSON] et al. [35] used ZC in time domain and mean MES, CC and, DCC (Delta Cepstrum Coefficients) in frequency domain. The last one is defined as a characteristic of the dynamic type, and it is the difference between two CC. The features were used to classify seven hand movements, and they obtained a precision of 91-94.4%, depending on the test subject. This classification was used for digital robotic arm control, with 62.5 Hz delay. In addition, [PERSON] et al. [33] presented a method for automatic detection of posture transition and used it for a knee-ankle-foot orthosis. Furthermore, they used a PCA for dimensionality reduction from the eleven extracted features of ten subjects with 14 sensors (MAV, SSC, STD, entropy, coefficient of variation, maximum, minimum, median, maximum to RMS ratio, RMS to mean ratio, and fractal dimension). The obtained precision was 92.94% for the detection of the sit-to-stand posture transition. In the same way, [PERSON] et al. [15] compounded IEMG, STD and RMS features in time domain and MPF and MNF in frequency domain. Besides using a linear kernel, the dimensionality reduction was done by PCA. Starting from seven vectors, the obtained results were higher than 92.25% for classifying eight movements, whose signals were extracted using the eight channels from Myo armband device. Furthermore, [PERSON] et al. [7] combined five time domain features (LOG, DASDV, VAR, ZC and MYOP) with two in frequency domain (MNF and FR) to record the activity from 47 healthy subjects when swallowing water, yogurt and saliva, using for classification SVM with RBF kernel, and obtained 92.03% accuracy. Moreover, without feature extraction tools, [PERSON] et al. [19] extracted the synergistic patterns of myoelectrical activities by a non-negative matrix factorization (NMF), with five healthy subjects, to classify five different movements (hand open and close, key pinch, palm valgus and grasp cylindrical tool). They implemented two different filters, one analog and one digital, within the recorded signals from six muscles. By their method, the muscle synergy patterns as a feature vector matrix could achieve the mean classification rate of 96.08%. Vector Data Description (SVDD), obtaining 79.54% accuracy. In the same year, [PERSON] and [PERSON] [64] used LS-SVM to differentiate between the state of high mental load and fatigue, by combining EEG, ECG, and Electrooccupogram (EOG) signals, with a feature reduction made with a Recursive Feature Elimination (FRE) and keeping RBF kernel. This procedure improved the accuracy to 92.67% compared against their previous study.