Smart Learning System Based on EEG Signals

  • Aaditya SharmaEmail author
  • Swadha GuptaEmail author
  • Sawinder KaurEmail author
  • Parteek KumarEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)


According to recent trends in information technology, classroom learning is transformed to Web based learning. This transformation helps learner to trigger digital technologies anywhere and anytime. This paper plan to build a system that can harness the power of the brain and build smart and meaningful applications to make life easier. The major problem is emerged during online education is loose the learner’s active attention after some duration of time. This leads to the user getting distracted without having any mechanism to provide him with a feedback, as a result, online learning is not getting as much effective as classroom learning. Therefore, EEG device is used for data acquisition, to measure EEG signals and also to monitor the attention levels of user. Proposed project will collect the EEG data to calculate various parameters such as concentration level, attention level, etc. These parameters will be used in the smart applications to provide real-time analysis and feedback to the user. This technology will provide real-time feedback user who has enrolled in MOOCs. This should foresee whether the student struggles or not while learning to give convenient alarms.


E-learning EEG MOOCs Classroom education 



This Publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).


  1. 1.
    Penaloza, C., Mae, Y., Cuellar, F., Kojima, M., Arai, T.: Brain machine interface system automation considering user preferences and error perception feedback. In: IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4 (2014)CrossRefGoogle Scholar
  2. 2.
    Blankertz, B., Dornhege, G., Lemm, S., Krauledat, M., Curio, G., Müller, K.: The Berlin brain-computer interface: machine learning based detection of user specific brain states. J. Univ. Computer. Sci. 12(6), 581–607 (2006)Google Scholar
  3. 3.
    Millan, J.: On the need for on-line learning in brain-computer interfaces. MIT Press 38(4), 34–41 (2004)Google Scholar
  4. 4.
    Dornhege, G., Millán, J., Hinterberger, T., McFarland, D., Müller, K.: Toward Brain-Computer Interfacing. MIT Press, Cambridge (2007)CrossRefGoogle Scholar
  5. 5.
    Dornhege, G., Krauledat, M., Müller, K., Blankertz, B.: General signal processing and machine learning tools for BCI. In: Toward Brain-Computer Interfacing, pp. 207–233. MIT Press, Cambridge (2007)Google Scholar
  6. 6.
    Katona, J., Ujbanyi, T., Sziladi, G., Kovari, A.: Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based brain-computer interface. In: 7th IEEE International Conference on Cognitive Infocommunications, pp. 000251–000257 (2016)Google Scholar
  7. 7.
    Ekandem, J., Davis, T., Alvarez, I., James, M., Gilbert, J.: Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics 55, 592–598 (2012)CrossRefGoogle Scholar
  8. 8.
    Bright, D., Nair, A., Salvekar, D., Bhisikar, S.: EEG-based brain controlled prosthetic arm. In: Advances in Signal Processing (CASP), pp. 479–483 (2016)Google Scholar
  9. 9.
    Tiwari, K., Saini, S.: Brain controlled robot using neurosky mindwave. J. Technol. Adv. Sci. Res. 1(4), 328–331 (2015)Google Scholar
  10. 10.
    Ang, A., Zhang, Z., Hung, Y., Mak, J.: A user-friendly wearable single-channel EOG-based human-computer interface for cursor control. In: IEEE Engineering in Medicine and Biology Society Conference on Neural Engineering Montpellier, April 2015Google Scholar
  11. 11.
    Blondet, M., Badarinath, A., Khanna, C., Jin, Z.: A wearable real-time BCI system based on mobile cloud computing. IEEE, January 2014Google Scholar
  12. 12.
    Dernoncourt F.: Replacing the computer mouse. In: Presented at the Boston Accessibility Conference, Cambridge, USA (2012)Google Scholar
  13. 13.
    Stinson, B., Arthur, D.: A novel EEG for alpha brain state training, neurobiofeedback and behavior change. Complement. Ther. Clin. Pract. 19, 114–118 (2013)CrossRefGoogle Scholar
  14. 14.
    Ghodake, A., Shelke, S.D.: Brain controlled home automation system. In: 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–4 (2016)Google Scholar
  15. 15.
    Eldenfria, A., Al-Samarraie, H.: The effectiveness of an online learning system based on aptitude scores: an effort to improve students’ brain activation. Educ. Inf. Technol. 2019, 1–15 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

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