CFP last date
22 April 2024
Reseach Article

Automatic Exam Evaluation based on Brain Computer Interface

by Hameda F. Balat, M.A. El-dosuky, El-Saeed M. Abd El-Razek, Magdi Z. Rashed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 25
Year of Publication: 2020
Authors: Hameda F. Balat, M.A. El-dosuky, El-Saeed M. Abd El-Razek, Magdi Z. Rashed
10.5120/ijca2020920792

Hameda F. Balat, M.A. El-dosuky, El-Saeed M. Abd El-Razek, Magdi Z. Rashed . Automatic Exam Evaluation based on Brain Computer Interface. International Journal of Computer Applications. 175, 25 ( Oct 2020), 15-21. DOI=10.5120/ijca2020920792

@article{ 10.5120/ijca2020920792,
author = { Hameda F. Balat, M.A. El-dosuky, El-Saeed M. Abd El-Razek, Magdi Z. Rashed },
title = { Automatic Exam Evaluation based on Brain Computer Interface },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2020 },
volume = { 175 },
number = { 25 },
month = { Oct },
year = { 2020 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number25/31606-2020920792/ },
doi = { 10.5120/ijca2020920792 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:26:05.333014+05:30
%A Hameda F. Balat
%A M.A. El-dosuky
%A El-Saeed M. Abd El-Razek
%A Magdi Z. Rashed
%T Automatic Exam Evaluation based on Brain Computer Interface
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 25
%P 15-21
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain computer interface applications can be used to overcome learning problems, especially student anxiety, lack of focus, and lack of attention. This paper introduces a system based on brain computer interface (BCI) to be used in education to measure intended learning outcomes and measure the impact of noise on the degree of system accuracy. This system works online and is based on recorded brain signal dataset. The system can be considered as a special case of P300 speller accepting only letters from A to D. These are the possible answers to multiple-choice questions MCQ. The teacher makes exams, stores them in an exam database and delivers them to students. Students enroll into the system and record their brain signals. Brain signals go through preprocessing phase in which signals undergo low and high pass filter. Then the signals undergo a subsampling and segmentation. The features obtained are used as inputs to Linear Discriminant Analysis (LDA). Gained accuracy is 91%.

References
  1. Katona, J., & Kovari, A. (2016). A Brain–Computer Interface Project Applied in Computer Engineering. IEEE Transactions on Education, 59(4), 319-326.
  2. Verkijika, S. F., & De Wet, L. (2015). Using a brain-computer interface (BCI) in reducing math anxiety: Evidence from South Africa. Computers & Education, 81, 113-122.
  3. Mannan, M. M. N., Kamran, M. A., & Jeong, M. Y. (2018). Identification and removal of physiological artifacts from electroencephalogram signals: A review. IEEE Access, 6, 30630-30652
  4. Guger, C., Allison, B. Z., & Mrachacz-Kersting, N. (2019). Brain-Computer Interface Research: A State-of-the-Art Summary 7. In Brain-Computer Interface Research (pp. 1-9). Springer, Cham.
  5. Weiskopf, N., Mathiak, K., Bock, S. W., Scharnowski, F., Veit, R., Grodd, W., ... & Birbaumer, N. (2004). Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE transactions on biomedical engineering, 51(6), 966-970
  6. Schomer, D. L., & Da Silva, F. L. (2012). Niedermeyer's electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.
  7. Duffy, F. H., Hughes, J. R., Miranda, F., Bernad, P., & Cook, P. (1994). Status of quantitative EEG (QEEG) in clinical practice, 1994. Clinical Electroencephalography, 25(4), vi-xxii.
  8. Verkijika, S. F., & De Wet, L. (2015). Using a brain-computer interface (BCI) in reducing math anxiety: Evidence from South Africa. Computers & Education, 81, 113-122.
  9. Katona, J., & Kovari, A. (2018). Examining the learning efficiency by a brain-computer interface system. Acta Polytechnica Hungarica, 15(3), 251-280.
  10. Crawford, C. S., Gardner-McCune, C., & Gilbert, J. E. (2018, February). Brain-computer interface for novice programmers. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 32-37). ACM.
  11. Arnaldo, R. M., Iglesias, J., Gómez, V. F., Crespo, J., Pérez, L., Alonso, J. F., & Sanz, A. R. (2018, January). Computerized Brain Interfaces for Adaptive Learning and Assessment. In International Conference on Intelligent Human Systems Integration (pp. 237-241). Springer, Cham.
  12. Zammouri, A., Moussa, A. A., & Mebrouk, Y. (2018). Brain-computer interface for workload estimation: assessment of mental efforts in learning processes. Expert Systems with Applications, 112, 138-147
  13. Speier, W., Deshpande, A., & Pouratian, N. (2015). A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems. Clinical Neurophysiology, 126(6), 1171-1177.
  14. Yamamoto, Y., Yoshikawa, T., & Furuhashi, T. (2015). Improvement of performance of Japanese P300 speller by using second display. Journal of Artificial Intelligence and Soft Computing Research, 5(3), 221-226.
  15. Li, Q., Shi, K., Ma, S., & Gao, N. (2016, August). Improving classification accuracy of SVM ensemble using random training set for BCI P300-speller. In 2016 IEEE International Conference on Mechatronics and Automation (pp. 2611-2616). IEEE.
  16. Jin, J., Sellers, E. W., Zhou, S., Zhang, Y., Wang, X., & Cichocki, A. (2015). A P300 brain–computer interface based on a modification of the mismatch negativity paradigm. International journal of neural systems, 25(03), 1550011.
  17. Lin, Z., Zhang, C., Zeng, Y., Tong, L., & Yan, B. (2018). A novel P300 BCI speller based on the Triple RSVP paradigm. Scientific reports, 8(1), 3350.
  18. Chaurasiya, R. K., Londhe, N. D., & Ghosh, S. (2015, September). An efficient P300 speller system for Brain-Computer Interface. In 2015 International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 57-62). IEEE
  19. Mubeen, M. A. (2016). Bayesian signal detection and source separation in simulated brain computer interface systems (Order No. 10109594). Available from ProQuest Dissertations & Theses Global. (1796359048). Retrieved from https://search.proquest.com/docview/1796359048?accountid=178282
  20. slam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4-5), 287-305.
  21. Meziani, A., Djouani, K., Medkour, T., & Chibani, A. (2019). A Lasso quantile periodogram based feature extraction for EEG-based motor imagery. Journal of Neuroscience Methods, 328, 108434.
  22. Jin, J., Miao, Y., Daly, I., Zuo, C., Hu, D., & Cichocki, A. (2019). Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Networks, 118, 262-270.
  23. Joadder, M. A., Siuly, S., Kabir, E., Wang, H., & Zhang, Y. (2019). A new design of mental state classification for subject independent BCI systems. IRBM, 40(5), 297-305.
  24. Mateo, J., Torres, A. M., García, M. A., & Santos, J. L. (2016). Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method. Neural Computing and Applications, 27(7), 1941-195.
  25. Aithal, P. S., & Kumar, P. M. (2016). Student performance and Learning Outcomes in Higher Education Institutions. International Journal of Scientific Research and Modern Education (IJSRME), 1, 674-684.
  26. Webb, M., & Doman, E. (2016). Does the Flipped Classroom Lead to Increased Gains on Learning Outcomes in ESL/EFL Contexts?. CATESOL Journal, 28(1), 39-6.
  27. Lee, N. J., Chae, S. M., Kim, H., Lee, J. H., Min, H. J., & Park, D. E. (2016). Mobile-based video learning outcomes in clinical nursing skill education: a randomized controlled trial. Computers, Informatics, Nursing, 34(1), 8.
  28. Chen, K. S., Monrouxe, L., Lu, Y. H., Jenq, C. C., Chang, Y. J., Chang, Y. C., & Chai, P. Y. C. (2018). Academic outcomes of flipped classroom learning: a meta‐analysis. Medical education, 52(9), 910-924.
  29. Minguillon, J., Lopez-Gordo, M. A., & Pelayo, F. (2017). Trends in EEG-BCI for daily-life: Requirements for artifact removal. Biomedical Signal Processing and Control, 31, 407-418.
  30. International 10-20 system , https://commons.wikimedia.org/wiki/File:21_electrodes_of_International_10-20_system_for_EEG.svg
  31. https://www.emotiv.com/epoc/
Index Terms

Computer Science
Information Sciences

Keywords

Brain Computer interface (BCI) Intended learning outcomes Education.