Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces

David Balderas, Pedro Ponce, Diego Lopez-Bernal and Arturo Molina
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David Balderas: Tecnologico de Monterrey National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
Pedro Ponce: Tecnologico de Monterrey National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
Diego Lopez-Bernal: Tecnologico de Monterrey National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
Arturo Molina: Tecnologico de Monterrey National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico

Future Internet, 2021, vol. 13, issue 8, 1-27

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.

Keywords: Education 4.0; artificial intelligence; classification methods; signal processing; brain computer interfaces; testbench (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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