EEG acquisition and analysis to improve stochastic processes and signal processing understanding in Engineering students: refining active learning dynamics via interactive approach in teaching

  • Ricardo Zavala YoéEmail author
  • Ricardo A. Ramírez Mendoza
Original Paper


Mathematics as stochastic processes, signal processing and dynamical systems may be difficult to understand even for Engineering students. So, in order to improve assimilation of their contents, we proposed to use active learning (AL) in a novel way. AL will be linked to an electroencephalographic signals device in order to motivate students through real life situations. Before starting our research with massive groups of people, we preferred to implement a pilot study with two groups of advantaged students for 1 year. They were compared with 4 groups of students undergoing a traditional learning process (corresponding to 2 years of class; specifically, two groups per year in 2 years). Our study, in this original paper, aligns with the educational Tec21 model of Tecnologico de Monterrey in a novel and unique way by improving AL dynamics via interactive approach in teaching.


Active learning Data acquisition Educational innovation Electroencephalographic signals Stochastic processes Signal processing Scientific computational analysis Interactive approach in teaching 



The authors would like to acknowledge the financial and the technical support of Writing Lab, TecLabs, Tecnologico de Monterrey, in the production of this work.


  1. 1.
    Anitha, H.M., Anusha, N.R.: Active learning techniques in engineering education. Int. J. Res. Eng. Technol. 3(11), 462–465 (2014)CrossRefGoogle Scholar
  2. 2.
    Barnes, D.: Active Learning. Leeds University, TVEI Support Project (1989). ISBN 978-1-872364-00-1Google Scholar
  3. 3.
    Batia, A.: Active learning pedagogies promoting the art of structural and civil engineering. In: 122nd Annual Conference and Exposition, June 14–17, Seattle Wash., USA (2015)Google Scholar
  4. 4.
    Bennett, B., Rolheiser, C.: Cooperative learning: where heart meets mind, published by Educational Connections, Toronto, ON, Canada, and Professional Development Associates, Washington, USA (1991)Google Scholar
  5. 5.
    Crouch, C., Mazur, E.: Peer instruction: ten years of experience and results. Am. J. Phys. 69(9), 970 (2001)CrossRefGoogle Scholar
  6. 6.
    Freeman, S., et al.: Active learning increases student performance in science, engineering and mathematics. PNAS 111(23), 8410–8415 (2014)CrossRefGoogle Scholar
  7. 7.
    Friesen, N.: Report: defining blended (online) (2014).
  8. 8.
    Gil-Nagel, A.: Manual de Electroencefalografıa. McGraw-Hill Interamericana, Mexico (2001)Google Scholar
  9. 9.
    g.tec Medical Engineering Gmbh: Advanced Bio-signal Acquisition Processing and Analysis, Products 2013/2014.
  10. 10.
    Hall, S., et al.: Adoption of active learning in a lecture-based engineering class. In: Proceedings of the 32nd ASEE/IEEE Frontiers in Education Conference, Nov. 6–9, Boston, MA, 2002.
  11. 11.
    Kladoss, M., et al.: An automatic EEG based system for the recognition of math anxiety. In: IEEE 30th International Symposium on Computer Based Medical Systems (2017)Google Scholar
  12. 12.
    Kornhauser, Z.: Active Learning and Assessment, Symposium on the Use and Assessment of Active Learning in Mathematics, Columbia Center for Teaching and Learning, Aug. 3, 2016Google Scholar
  13. 13.
    Kyriacou, C.: Active learning in secondary school mathematics. Br. Educ. Res. J. 18(3), 309–318 (1992)CrossRefGoogle Scholar
  14. 14.
    León, A., Davis, L., Kraemer, H.: The role and interpretation of pilot studies in clinical research. J. Psychiatr. Res. 45(5), 626–629 (2011)CrossRefGoogle Scholar
  15. 15.
    Mikosch, T.: Elementary stochastic calculus with finance in view. In: Advanced Series on Statistical Science and Applied Probability, vol. 6, World Scientific, Singapore, Oct. 1998Google Scholar
  16. 16.
    Price, M.: Does active learning work? A review of the research. J. Eng. Educ. 93, 223–231 (2004)CrossRefGoogle Scholar
  17. 17.
    Senior, C., Howard, C.: The state of the art in student engagement. Front. Psychol. 6, 355 (2015)CrossRefGoogle Scholar
  18. 18.
    Stoica, P.: Spectral Analysis of Signals. Prentice Hall, Prentice (2005)zbMATHGoogle Scholar
  19. 19.
    The Mathworks, Matlab. The Language of Technical Computing, The Mathworks (2012)Google Scholar
  20. 20.
    Wang, A.-L.: How much can be taught about Stochastic Processes and to whom? In: Batanero, C. (ed.) Training Researchers in the Use of Statistics, pp. 73–85. International Association for Statistical Education and International Statistical Institute, Granada (2001)Google Scholar
  21. 21.
    Bonwell, C., Eison, J.: Active learning: creating excitement in the classroom. Information Analyses. ERIC Clearinghouse Products (071).
  22. 22.
    Tecnologico de Monterrey: Internet web page about AL and ALC.
  23. 23.
    Tecnológico de Monterrey: EduTrends Reports.
  24. 24.
    Yale University Center for Teaching and Learning.
  25. 25.
    Zavala-Yoé, R., Ramírez-Mendoza, R.A.: Dynamische Entropie-Trajektorien zum gleichzeitigen Vergleich von Patienten mit Doose und Lennox-Gastaut Syndrome. Springer Medizin, Zeitschrift für Epileptologie (2019)Google Scholar
  26. 26.
    Zavala-Yoé, R., Ramírez-Mendoza, R., Morales-Menendez, R.: Real time acquisition and processing of massive electroencephalographic signals for modeling by nonlinear statistics. Int. J. Int. Des. Manuf. 2019(32), 17–18 (2016)Google Scholar
  27. 27.
    Zavala-Yoé, R., Ramírez-Mendoza, R.A.: Dynamic complexity measures and entropy paths for modeling and comparison of evolution of patients with drug resistant epileptic encephalopathy syndromes (DREES). Metab. Brain Dis. 32, 1553–1569 (2017)CrossRefGoogle Scholar
  28. 28.
    Zavala-Yoé, R., Ramírez-Mendoza, R.A., Retrospektive inter-und intrapatientale Evaluation von Epileptischen Enzephalopathien durch Synchronisierten Vergleich von Dynamischen Komplexitätsmaßen des Langzeit EEG. Springer Medizin, Zeitschrift für Epileptologie (2018)Google Scholar

Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  • Ricardo Zavala Yoé
    • 1
    Email author
  • Ricardo A. Ramírez Mendoza
    • 1
  1. 1.Escuela de Ingeniería y CienciasTecnologico de MonterreyMexico CityMexico

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