Cover
Vol. 17 No. 2 (2021)

Published: December 31, 2021

Pages: 38-45

Original Article

EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

Samaa S. Abdulwahab 1 Hussain K. Khleaf Manal H. Jassim
1Electrical Engineering Department, University of Technology, Iraq, Baghdad
History
  • Received: June 25, 2021
  • Revised: July 28, 2021
  • Accepted: July 29, 2021
  • Available Online: July 30, 2021
DOI

https://doi.org/10.37917/ijeee.17.2.5

Abstract

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

Keywords

References

  1. S. S. Abdulwahab, H. K. Khleaf, and M. H. Jassim, “A Systematic Review of Brain-Computer Interface Based EEG,” Iraqi J. Electr. Electron. Eng., vol. 16, no. 2, pp. 1–10, Dec. 2020, doi: 10.37917/ijeee.16.2.9.
  2. B. Osalusi, A. Abraham, and D. Aborisade, “EEG Classification in Brain Computer Interface ( BCI ): A Pragmatic Appraisal,” vol. 8, no. 1, pp. 1–11, 2018, doi: 10.5923/j.ajbe.20180801.01.
  3. A. Korik, R. Sosnik, N. Siddique, and D. Coyle, “Imagined 3D hand movement trajectory decoding from sensorimotor EEG rhythms,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, Feb. 2017, pp. 4591–4596, doi: 10.1109/SMC.2016.7844955.
  4. Z. M. Alhakeem and R. Ali, “Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals,” Iraqi J. Electr. Electron. Eng., vol. 15, no. 1, pp. 13–27, Jun. 2019, doi: 10.37917/ijeee.15.1.2.
  5. S. N. Abdulkader, A. Atia, and M. S. M. Mostafa, “Brain computer interfacing: Applications and challenges,” Egypt. Informatics J., vol. 16, no. 2, pp. 213–230, Jul. 2015, doi: 10.1016/j.eij.2015.06.002.
  6. S. Snyder and X. A. Shen, “A Review of Brain Signal Processing Methods,” Int’l Conf. Biomed. Eng. Sci., vol. BIOENG’17, pp. 10–16, 2017.
  7. X. Zhang, L. Yao, X. Wang, W. Zhang, S. Zhang, and Y. Liu, “Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks,” no. February, 2018, [Online]. Available: http://arxiv.org/abs/1802.03996.
  8. N. M. Firouz and S. Haghipour, “A Survey on EEG Signal Classification with Neural Network for Brain Computer Interface Applications,” Int. J. Comput. Inf. Technol., vol. 4, no. 2, pp. 34–42, 2016.
  9. R. Abiri, S. Borhani, E. W. Sellers, Y. Jiang, and X. Zhao, “A comprehensive review of EEG-based brain-computer interface paradigms,” J. Neural Eng., vol. 16, no. 1, p. 011001, Feb. 2019, doi: 10.1088/1741-2552/aaf12e.
  10. N. Padfield, J. Zabalza, H. Zhao, V. Masero, and J. Ren, “EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges,” Sensors (Switzerland), vol. 19, no. 6, pp. 1–34, 2019, doi: 10.3390/s19061423.
  11. D. Bansal and R. Mahajan, EEG-Based Brain-Computer Interfacing (BCI). Elsevier Inc., 2019.
  12. R. A. Ramadan and A. V. Vasilakos, “Brain computer interface: control signals review,” Neurocomputing, vol. 223, no. October 2016, pp. 26–44, Feb. 2017, doi: 10.1016/j.neucom.2016.10.024.
  13. J. D. R. Millán et al., “Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges,” Frontiers in Neuroscience, vol. 4, no. SEP. 2010, doi: 10.3389/fnins.2010.00161.
  14. H. Zhou, “Principles and Applications in Brain Computer Interfaces,” no. March, pp. 1–13, 2018.
  15. N. Tiwari, D. R. Edla, S. Dodia, and A. Bablani, “Brain computer interface: A comprehensive survey,” Biol. Inspired Cogn. Archit., vol. 26, no. October, pp. 118–129, 2018, doi: 10.1016/j.bica.2018.10.005.
  16. V. G. Sangam, S. B. M, S. S. Raman, A. S. Lakshmi, P. A. Murthy, and M. Faizan, “Electroencephalogram ( EEG ), its Processing and Feature Extraction,” no. June, 2020, doi: 10.17577/IJERTV9IS060814.
  17. B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, “xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain-Computer Interface,” IEEE Trans. Biomed. Eng., vol. 56, no. 8, pp. 2035–2043, 2009, doi: 10.1109/TBME.2009.2012869.
  18. N. Brodu, F. Lotte, and A. Lécuyer, “Comparative study of band-power extraction techniques for Motor Imagery classification,” in IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CCMB 2011: 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, 2011, pp. 95–100, doi: 10.1109/CCMB.2011.5952105.
  19. A. Khosla, P. Khandnor, and T. Chand, “A comparative analysis of signal processing and classification methods for different applications based on EEG signals,” Biocybern. Biomed. Eng., vol. 40, no. 2, pp. 649–690, 2020, doi: 10.1016/j.bbe.2020.02.002.
  20. J. Meng, S. Zhang, A. Bekyo, J. Olsoe, B. Baxter, and B. He, “Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks,” Sci. Rep., vol. 6, p. 38565, Dec. 2016, doi: 10.1038/srep38565.
  21. A. Al-saegh, “Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data,” Iraq J. Electr. Electron. Eng., vol. 15, no. 1, pp. 1–12, Jun. 2019, doi: 10.37917/ijeee.15.1.1.
  22. A. Al-Saegh, S. A. Dawwd, and J. M. Abdul-Jabbar, “Deep learning for motor imagery EEG-based classification: A review,” Biomedical Signal Processing and Control, vol. 63. Elsevier Ltd, Jan. 01, 2021, doi: 10.1016/j.bspc.2020.102172.
  23. H. Wang, Y. Li, J. Long, T. Yu, and Z. Gu, “An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface,” Cogn. Neurodyn., vol. 8, no. 5, pp. 399–409, Oct. 2014, doi: 10.1007/s11571-014-9296-y.
  24. S. Selim, M. Tantawi, H. Shedeed, and A. Badr, “Reducing execution time for real-time motor imagery based BCI systems,” in Advances in Intelligent Systems and Computing, Oct. 2017, vol. 533, pp. 555–565, doi: 10.1007/978-3-319-48308-5_53.
  25. Q. Huang, Z. Zhang, T. Yu, S. He, and Y. Li, “An EEG-/EOG-Based Hybrid Brain-Computer Interface: Application on Controlling an Integrated Wheelchair Robotic Arm System,” Front. Neurosci., vol. 13, p. 1243, Nov. 2019, doi: 10.3389/fnins.2019.01243.
  26. S. Selim, M. M. Tantawi, H. A. Shedeed, and A. Badr, “A CSPAM-BA-SVM Approach for Motor Imagery BCI System,” IEEE Access, vol. 6, pp. 49192–49208, Aug. 2018, doi: 10.1109/ACCESS.2018.2868178.
  27. N. Padfield, J. Zabalza, H. Zhao, V. Masero, and J. Ren, “EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges,” Sensors (Switzerland), vol. 19, no. 6, p. 1423, Mar. 2019, doi: 10.3390/s19061423.
  28. R. D. Roy, Amit Konar, and N. Tibarewala, “CONTROL OF ARTIFICIAL LIMB USING EEG & EMG-A REVIEW.”
  29. M. Kołodziej, A. Majkowski, and R. J. Rak, “A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6593 LNCS, no. PART 1, pp. 280–289, doi: 10.1007/978-3-642-20282-7_29.
  30. D. R. Edla, M. F. Ansari, N. Chaudhary, and S. Dodia, “Classification of Facial Expressions from EEG signals using Wavelet Packet Transform and SVM for Wheelchair Control Operations,” in Procedia Computer Science, Jan. 2018, vol. 132, pp. 1467–1476, doi: 10.1016/j.procs.2018.05.081.
  31. H. Yuan, A. Doud, A. Gururajan, and B. He, “Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 16, no. 5, pp. 425–431, Oct. 2008, doi: 10.1109/TNSRE.2008.2003384.
  32. O. Attallah, J. Abougharbia, M. Tamazin, and A. A. Nasser, “A bci system based on motor imagery for assisting people with motor deficiencies in the limbs,” Brain Sci., vol. 10, no. 11, pp. 1–25, Nov. 2020, doi: 10.3390/brainsci10110864.
  33. J. Kevric and A. Subasi, “Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system,” Biomed. Signal Process. Control, vol. 31, pp. 398–406, Jan. 2017, doi: 10.1016/j.bspc.2016.09.007.