System Development for Automatic Control Using BCI

  • Antonio MezaEmail author
  • Rosario Baltazar
  • Miguel Casillas
  • Víctor Zamudio
  • Francisco Mosiño
  • Bladimir Serna
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 148)


Brain-Computer Interface (BCI for its acronym in English) is a device that allow the communication between a user and adapted environment. The Ambient Assisted Living (AAL for its acronym in English) can be potentially used to assist people with some motor disability. In this article, we show the low-cost system development that permit an actuator control through commercial EEG signal acquisition, detecting a flickering. The system is also tested to evaluate its feasibility with an offline analysis in Matlab. The experiments and results are shown.


EEG (Electroencephalogram) Embedded systems Brain-computer interface Muse FFT 



To CONACYT, for their support during the master degree process and research stay in Valencia, Spain. To “Persianas de los altos”, for their support with materials and Guanajuato’s government for giving us their support to complete this research.


  1. 1.
    Mora, N., et al.: Controlling AAL environments through BCI. In: MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings (2014).
  2. 2.
    Lotte, F., et al.: Exploring large virtual environments by thoughts using a brain-computer interface based on motor imagery and high-level commands. Presence: Teleoperators Virtual Environ. 19(1), 54–70 (2010). ISSN: 10547460. Scholar
  3. 3.
    Sakamaki, I., et al.: Assistive technology design and preliminary testing of a robot platform based on movement intention using low-cost brain computer interface. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2243–2248 (2017). ISSN: 1062-922XGoogle Scholar
  4. 4.
    Belwafi, K., et al.: An embedded implementation of home devices control system based on brain computer interface. In: Proceedings of the International Conference on Microelectronics, ICM2015-March, pp. 140–143 (2014).
  5. 5.
    Belwafi, K., et al.: Online adaptive filters to classify left and right hand motor imagery. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 335–339 (2016).
  6. 6.
    Belwafi, K., et al.: An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. In: IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014:2014 IEEE Symposium on Computational Intelligence, Cognitive Algo-rithms, Mind, and Brain, Proceedings, pp. 121–126 (2014).
  7. 7.
    Ianosi-Andreeva-Dimitrova, A., Mândru, D.S., Abrudean, A.: A hybrid brain-computer interface implementation for the control of an assistive device. In: 2017 E-Health and Bioengineering Conference, EHB 2017, pp. 543–546 (2017).
  8. 8.
    Mora, N., et al.: Plug and play brain–computer interfaces for effective active and assisted living control. Med. Biol. Eng. Comput. 55(8), 1339–1352 (2017). ISSN: 17410444. Scholar
  9. 9.
    Belwafi, K., et al.: A hardware/software prototype of EEG-based BCI system for home device control. J. Signal Process. Syst. 89(2), 263–279 (2017). ISSN: 19398115. Scholar
  10. 10.
    Cantillo-Negrete, J., et al.: Control signal for a mechatronic hand orthosis aimed for neurore habilitation. In: Pan American Health Care Exchanges, PAHCE2015-July, pp. 1–4 (2015). ISSN: 2327817X.
  11. 11.
    Prataksita, N., et al.: Brain-robot control interface: development and application. In: 2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014, pp. 10–13 (2014).
  12. 12.
    Looned, R., et al.: Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. J. Neuroeng. Rehabil. 11 (2014)CrossRefGoogle Scholar
  13. 13.
    Yu, G., et al.: EEG-based brain-controlled lower extremity exoskeleton rehabilitation robot. In: 2017 IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2017 and IEEE Conference on Robotics, Automation and Mechatronics, RAM 2017 - Proceedings 2018-January, pp. 763–767 (2018).
  14. 14.
    Several Authors. Technical specifications, validation, and research use (2015).
  15. 15.
    Moctezuma, L.A., Molinas, M., Torres-García, A.A.: Towards an API for EEG-Based Imagined Speech classification (2018)Google Scholar
  16. 16.
    Youssef Ali Amer, A., Wittevrongel, B., VanHulle, M.M.: Accurate decoding of short, phase-encoded SSVEPs. Sensors (Switzerland) 18(3), 1–9 (2018). ISSN: 14248220. Scholar
  17. 17.
    Biomedical signal. Development of a MATLAB-based toolbox for BCI applications in VR, pp. 1579–1583 (2012)Google Scholar
  18. 18.
    Charles, K.: Alexander and Matthew N. O. Sadiku. Fundamentos de cir-cuitos el ectricos. 3rd edn, p. 1051. McGraw Hill (2004). ISBN: 0-07-326800-3Google Scholar
  19. 19.
    Espressif systems. Development boards (2018).
  20. 20.
    Espressif systems. ESP32 at instruction set and examples (2018). ISSN: 03614409Google Scholar
  21. 21.
    Hopcroft, J., Motwani, R., Ullman, J.: Introduccion a la teorııa de automatas, lenguajes y computación, p. 452 (2008). ISBN: 978-847-829-0888Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Antonio Meza
    • 1
    Email author
  • Rosario Baltazar
    • 1
  • Miguel Casillas
    • 1
  • Víctor Zamudio
    • 1
  • Francisco Mosiño
    • 1
  • Bladimir Serna
    • 1
  1. 1.Instituto Tecnológico de LeónLeón, GuanajuatoMéxico

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