# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification
### SVM-Based Myoelectric Signal Classification
Sometimes, authors combine features in the time domain with those in the frequency domain. [PERSON] et al. [47] developed and tested a tongue interface to detect six motions, including saliva swallowing, from the surface of suprahyoid muscles at the underside of the jaw. They combined RMS from time domain with CC features from frequency domain and achieved 95.1 \(\pm\) 1.9% classification accuracy. In addition, [PERSON] et al. [36] performed a classification of eight facial expressions. They used 74 features for each expression using only six channels, and achieved 99.6% of overall accuracy with a cubic kernel in SVM classifier. Among the features used, they included mean value and RMS value of all channels mean values. Nevertheless, other authors prefer to work in time-frequency domain. [PERSON] et al. [11] used the representation space of characteristic vector based on DWT (Discrete Wavelet Transform), by using an unrestricted parametrization of Wavelet mother. With this method, they obtained an average classification error of six hand movements, through eight electrodes, of \(4.7\pm 3.7\%\). Using the same method, [PERSON] et al. [43] utilized a pair of features after applying DWT in their MES raw data, from eight subjects, to identify the movement intention of the patients. They found as the best choice the fifth step in DWT decomposition combined with MAV and Max features, which achieved 100% accuracy with SVM classifier. In addition, [PERSON] et al. [21] classified 17 hand and wrist movements from MES signals acquired from NinaPro database. The feature vector was composed of RMS extracted from DWT and the average energy of spectrogram at each frequency bin, after having applied a Principal Component Analysis (PCA) and conserving the first three. By applying SVM, the highest classification accuracy was 95% and 71.3% for normally-limbed and amputee subjects, respectively. Moreover, [PERSON] et al. [17] used PCA for dimensionality reduction with a kernel quadratic in SVM classifier, where kurtosis, skewness, SSC, MAV and AR1 in TD were the features extracted. The overall mean classification accuracy was 99.04% for the two activities performed. At the other extreme, [PERSON] and [PERSON] [39] proposed an algorithm called GAPSO-SVM that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) combined with SVM. The algorithm selected optimal parameters for RBF and an optimal decomposition level also making use wavelet mother function to classify energy of wavelet coefficient obtained from forearm signals. In addition, they implemented PCA, and obtained 98.7% accuracy in the classification. As in the previous study, [PERSON] et al. [46] used PSO but they combined it with an improved SVM (by removing slack variables, as the bias value from the decision function), calling their method PSO-ISVM, which effectively identified six types of upper limb movements with an average recognition rate of 90.66%. As a feature vector, the WPT was used to extract the variance and energy of the wavelet packet coefficients and, as a kernel, the RBF. In the same manner, [PERSON] et al. [50] used the energy node of WPT coefficients as MES signal characteristic. They also used the Non-parametric Weighted Feature Extraction (NWFE) to reduce the dimensionality of features vector that enters the SVM, from which they obtained 98.39% precision in OAA mode, and 98.214% in OAO mode. After the analysis and comparison between different mother wavelet functions in discrete and continuous time, [PERSON] et al. [37] achieved 98.74% using MAV feature in Symlet 4 function in level 2, 98.49% by applying WL feature in Coif 3 function in level 4, both from DWT. For CWT, 98.56% was achieved with MAV by employing Sym 6, scale 16 and 98.64% with WL by using Mekh, scale 32. The authors classified 10 different hand movements with four channels.