## 2 State-of-the-Art Approaches
This heading includes methods with a special emphasis on \"deep learning\". In general, though, deep learning models can be roughly grouped into three main categories: unsupervised pretrained networks, convolutional neural networks, and recurrent neural networks. These three categories of models have already been used to analyse sEMG signals, as shown in the reviews of the preceding paragraphs. The authors concluded their extensive review by saying: \"A key challenge and impediment to the clinical deployment of deep learning methods is their high computational cost (i.e., long training times and high computational complexity). Because of the stringent power and size restrictions of prosthetic components, most devices are built using embedded systems\". On the other hand, the use of an SVM classifier for gestures in EEG and sEMG signals is still interesting, as shown by [PERSON] et al. 's review [PERSON] et al. (2018). Up to nine different variants of SVM classifiers were reviewed in several applications. The review showed that the accuracy of the 76 publications revised ranged from 18.83% to 100% with a mean of 80.8% and a standard deviation of 13.32%. Taking into account only the sEMG GR publications, the accuracy increases to a range from 73% to 100% with a mean of 91.1% and a standard deviation of 6.38%. The review concludes that \"SVMs result to be among the most versatile classifiers for pattern recognition... and furthermore resulted to be particularly suitable for online implementations\". Therefore, along with the trend to use deep learning, there is still interest in exploring the feasibility of class modelling with PLS-ECOC that simultaneously reduces the complexity associated with feature engineering and that associated with the deep learning approach. In addition, PLS-ECOC distinguishes between _failure_ and _error_. However, in all cases, the methods used are independent of the structure of \(\mathbf{X}\), the sEMG signals, and the encoding used to describe the classes, which is denoted by \(\mathbf{Y}\). In engineering, particularly in the field of signal transmission, redundancy has been used to distinguish, at the receiver, two signals despite having lost information during transmission. This idea has been used in this work to encode the gesture classes and construct the matrix \(\mathbf{Y}\) by assigning each class a code (a vector of ones and minus ones) using an ECOC matrix. Little attention has been paid to the codification of classes in GR sEMG publications. Only ref. [PERSON] et al. (2018) presents the effect of two codifications (one vs. one and one vs. all) which have been proven to be inefficient [PERSON] et al. (2018). The proposal in this work is to use PLS to find the linear relationship between the signals and the encoded classes, explaining the maximum variance in \(\mathbf{X}\), in \(\mathbf{Y}\), and the maximum correlation between them. PLS constructs _nol_ latent variables, linear combinations of \(\mathbf{X}\) variables and \(\mathbf{Y}\) variables under the condition of having the maximum correlation. In this way, PLS applies a reduction of the signal space but links to the classes of the gestures to be distinguished. This characteristic makes PLS an advance in the treatment of EMG signals. Since ancient times [PERSON] et al. (2018), the need to reduce the dimension of the signal space has been recognized, in most cases, as a second stage after obtaining features [PERSON] et al. (2018). In [PERSON] et al. (2018), using several classifiers, a comparison has been made between feature selection and dimensionality reduction, including PCA. However, in general, PCA is performed independently of the classifier and, therefore, of the classes to be modelled. In a review made, only one PLS application with sEMG signals has been found. It is devoted to model concurrent EEG and EMG data collected in a [PERSON]'s disease study [PERSON] (1965), but it was not a classification of gestures. The method, PLS-ECOC [PERSON] et al.