## 4 Pattern Recognition
### Restricted Boltzmann Machines
In the case of processing times, the training time is reduced using the limited frequency bands and regression method. This is a natural consequence of having a reduced dimensionality of data. Furthermore, while comparing the processing times between the presented methods, it can be observed that SOM has the largest ones, followed by SVM. The former could be due to the process of inserting the data one by one to adjust the map, which could be improved using a batch method. On the other hand, SVM needs to solve a quadratic optimization problem with a large data set and a small box constraint, which limits the algorithm convergence speed. Moreover, it can be observed that the BP method is slower than the RBM method. The reason behind this is that the ANN had many more neurons than the RBM (1000 neurons for BP over 64 neurons for RBM). However, other numbers of neurons for the BP did not give as high accuracy as those obtained. Finally, although LDA indeed is the fastest of all the presented algorithms by far, which is one of the reasons why its one of the most used methods for BCI, it has the problem of not being easily adaptable to a high number of classes, thus needing different methods for multi-class problems. ### Conclusions

With the aim of driving the development of competencies for future engineers and scientists, schools require curricula that is in line with the technological progress and demands of Industry 4.0. Consequently, Education 4.0 is searching for new ways to introduce students to emerging technologies, such as artificial intelligence, and how they are applied on real-life situations. Accordingly, Education 4.0 is responsible for helping future professionals begin being familiar with the area of artificial intelligence, and, at the same time, it must provide them with the opportunity of testing the acquired knowledge by applying it to real-life scenarios. Therefore, in the scope of the Education 4.0 framework, teachers and students are in need of updated educational material that helps them embrace their path towards teaching and learning more about the technologies that are being used in the incoming industrial revolution. Hence, the main objective of this work was to provide updated teaching/learning material that allows students to have an introduction to a cutting edge technology, such as BCI, which is used for several real-life applications, while providing them with the basic knowledge of five different AI techniques and how they can be applied over the basics of BCI experimentation. These different AI techniques were presented through a brief review of the methods and their corresponding pseudo-codes while also presenting the results of their implementation on BCI so that students become aware of the problems and possible outcomes of these experiments. This implementation was done over a test bench that consists on EEG and EOG recordings obtained from [37]. From the obtained results, it is important for students to notice that the obtained behavior of each method made sense with the information presented in Table 1; however, the main problem in this work was that none of them was able to always discriminate or even discriminate similarly to all subjects. Through the description of the AI techniques and the analysis of the results of applying them over the proposed BCI test bench, this work allows students to learn the basic theory of SOM, LDA, ANN-BP, SVM and RBM, as well as giving the guidelines for the application of those techniques over real-life BCI problems. Furthermore, teachers that are beginning to work under the Education 4.0 paradigm can use this work as introductory material to BCI and artificial intelligence, and the proposed test bench can be used by them as a reinforcement exercise or project to test the understanding of students posterior to a BCI or AI lesson. Notwithstanding of the contribution of this work to the development of updated curricula for the Education 4.0 framework, there is still a lot of work to do.