Abstract
Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0.
Highlights
IntroductionNew technologies are evolving at an exponential pace, and the consequential technological advancements achieved through them are blurring the lines between the physical, digital and biological worlds [1]
A similar effect occurs for the classification methods (Table 3), where the same subjects, plus subjects 1, 5 and 8, showed training and testing accuracy higher than chance level ≥60∼80% for Linear Discriminant Analysis (LDA), BP and Restricted Boltzmann Machines (RBM)
For the other subjects, it is not easy to discriminate between both classes, which is illustrated in Figure 13, where there is no observable difference between the EEG frequency response among these subjects
Summary
New technologies are evolving at an exponential pace, and the consequential technological advancements achieved through them are blurring the lines between the physical, digital and biological worlds [1]. These advancements constitute the basis of the fourth industrial revolution ( called Industry 4.0), which is principally constituted of progress in the areas of artificial intelligence (AI), robotics, nanotechnology, quantum computing, energy storage systems and the internet of the things (IoT) [2]. As Industry 4.0 continues changing the world, new challenges arise in different branches of society, one of them being education; Education 4.0 comes into existence
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
