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Diego López Bernal is an electromechanical engineer with a master’s degree in nanotechnology. He received both of these degrees from the Monterrey Institute of Technology and Higher Education in Mexico City. During his bachelor’s degree, he was enrolled on the Aeronautical Engineering Concentration, which he fully accredited through the Aeronautics Summer Session 2017 from the L’Université Fédérale Toulouse Midi-Pyrénées in France. Currently, he is studying a Ph.D. in Engineering Sciences, focusing on the artificial intelligence applied to medicine. Moreover, he is passionate about strength sports, especially powerlifting. He believes this sport is a discipline that helps him develop abilities, such as time management, teamwork, leadership, and resilience. His main achievements as a powerlifter include a Pan-American Bench Press Record and a North-American Championship, besides several National and Regional Championships.
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.
David Balderas; Pedro Ponce; Diego Lopez-Bernal; Arturo Molina. Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces. Future Internet 2021, 13, 202 .
AMA StyleDavid Balderas, Pedro Ponce, Diego Lopez-Bernal, Arturo Molina. Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces. Future Internet. 2021; 13 (8):202.
Chicago/Turabian StyleDavid Balderas; Pedro Ponce; Diego Lopez-Bernal; Arturo Molina. 2021. "Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces." Future Internet 13, no. 8: 202.
One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.
Diego Lopez-Bernal; David Balderas; Pedro Ponce; Arturo Molina. Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet 2021, 13, 193 .
AMA StyleDiego Lopez-Bernal, David Balderas, Pedro Ponce, Arturo Molina. Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet. 2021; 13 (8):193.
Chicago/Turabian StyleDiego Lopez-Bernal; David Balderas; Pedro Ponce; Arturo Molina. 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems." Future Internet 13, no. 8: 193.