Brain-computer interface for electric wheelchair based on alpha waves of EEG signal

• Autorzy: Banach Kacper , Małecki Mateusz , Rosół Maciej , Broniec Anna

Identyfikatory

DOI

10.1515/bams-2021-0095

• Języki publikacji: EN

Abstrakty

EN

Objectives: Helping patients suffering from serious neurological diseases that lead to hindering the independent movement is of high social importance and an interdisciplinary challenge for engineers. Brain–computer interface (BCI) interfaces based on the electroencephalography (EEG) signal are not easy to use as they require time consuming multiple electrodes montage. We aimed to contribute in bringing BCI systems outside the laboratories so that it could be more accessible to patients, by designing a wheelchair fully controlled by an algorithm using alpha waves and only a few electrodes. Methods: The set of eight binary words are designed, that allow to move forward, backward,turn right andleft, rotate 45° as well as toincrease and decrease the speed of the wheelchair. Our project includes: development of a mobile application which is used as a graphical user interface, real-time signal processing of the EEG signal, development of electric wheelchair engines control system and mechanical construction. Results: The average sensitivity, without training, was 79.58% and specificity 97.08%, on persons who had no previous contact with BCI. Conclusions: The proposed system can be helpful for people suffering from incurable diseases that make them closed in their bodies and for whom communication with the surrounding world is almost impossible.

Słowa kluczowe

EN

alpha waves; brain-computer interface; BCI; electroencephalography; EEG; wheelchair

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2021
• Tom: Vol. 17, no. 3
• Strony: 165--172
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Banach Kacper

• Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

autor

Małecki Mateusz

• Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

autor

Rosół Maciej

• Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

autor

Broniec Anna

• Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland, Phone: [phone]

• https://orcid.org/0000-0001-5321-4609

Bibliografia

• 1. Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 2007;4:32.
• 2. Shih JJ, Krusienski DJ, Wolpaw JR. Brain-computer interfaces in medicine. Mayo Clin Proc 2012;87:268.
• 3. Minguillonab J, Lopez-Gordocd MA, Pelayoa F. Trends in EEG-BCI for daily-life: requirements for artifact removal. Biomed Signal Process Contr 2017;31:407.
• 4. Deng LY, Hsu C-L, Lin T-C, Tuan J-S, Chang S-M. EOG-based Human-Computer Interface system development. Expert Syst Appl 2010;37:3337.
• 5. Nasreddine BA, Duk S, Hiroyuki K, Natsue Y, Yasuharu K. Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors. Biomed Signal Process Contr 2015;16:40.
• 6. Barreto A, Scargle S, Adjouadi M. A practical EMG-based humancomputer interface for users with motor disabilities. J Rehabil Res Dev 2011;37:53.
• 7. Pfurtscheller G, Woertz M, Muller G. Contrasting behavior of beta event-related synchronization and somatosensory evoked potential after median nerve stimulation during finger manipulation in man. Neurosci Lett 2002;323:113.
• 8. Wolpaw JR, Birbaumer N, Mc-Farland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002;113:767.
• 9. Neuper C, Muller GR, Kubler A. Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 2003; 114:399.
• 10. Hundia R. Brain computer interface - controlling devices utilizing the alpha brain waves. Int J Sci Tech Res 2015;4:281.
• 11. Fazel-Rezai R, Ahmad W. P300-based brain-computer interface paradigm design. In: Recent advances in brain-computer interface systems. North Dakota: IntechOpen; 2011.
• 12. Kuś R, Valbuena D, Zygierewicz J, Malechka T, Graeser A, Durka P. Asynchronous BCI based on motor imagery with automated calibration and neurofeedback training. IEEE Trans Neural Syst Rehabil Eng 2012;20:823.
• 13. Al-Qaysi Z, Zaidan BB, Zaidan AA, Suzani MS. A review of disability EEG based wheelchair control system: coherent taxonomy, open challenges and recommendations. Comput Methods Progr Biomed 2018;164:221.
• 14. Leeb R, Friedman D, Muller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci 2007;2007:79642.
• 15. Fattouh A, Horn O, Bourhis G. Emotional BCI control of a smart wheelchair. Int J Comput 2013;10:32-6.
• 16. Rebsamen B, Teo CL, Zeng Q, Ang MH, Burdet E, Guan C, et al. Controlling a wheelchair indoors using thought. IEEE Intell Syst 2007;07:1541.
• 17. Ferreira A, Silva RL, Celeste WC, Bastos TF, Sarcinelli M. Humanmachine interface based on muscular and brain signals applied to a robotic wheelchair. J Phys Conf 2007;90:012094.
• 18. Wang H, Li Y, Long J, Yu T, Gu Z. An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface. Cogn Neurosci 2014;8:399.
• 19. Luzheng B, Jinling L, Ke J, Ru L, Yili L. A speed and direction-based cursor control system with P300 and SSVEP. Biomed Signal Process Contr 2014;14:126.
• 20. Allison B, Brunner C, Altstatter C, Wagner IC, Grissmann S, Neuper C. A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control. J Neurosci Methods 2012;209:299.
• 21. Cao L, Li J, Hi J, Jiang C. A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control. J Neurosci Methods 2014;229:33.
• 22. Long J, Li Y, Wang H, Yu T, Pan J. Control of a simulated wheelchair based on a hybrid brain computer interface. Conf Proc IEEE Eng Med Biol Soc 2012;2012:6727-30. https://doi.org/10.1109/EMBC.2012.6347538.
• 23. Tanaka K, Matsunga K, Wang HO. Electroencephalogram based control of an electric wheelchair. IEEE Trans Robot 2005;21:762.
• 24. Ivanhoe CB, Eaddy NK. Encyclopedia of clinical neuropsychology. New York: Springer; 2011.
• 25. Nunez PL, Srinivasan R. Electric fields of the brain: the neurophysics of EEG, 2nd ed. Oxford: Oxford University Press; 2006.
• 26. Aminoff MJ. Chapter 3 - electroencephalography: general principles and clinical applications In: Aminoff’s electrodiagnosis in clinical neurology; 2012, vol. 37.
• 27. Van Loan C. Computational frameworks for the fast fourier transform. Front Appl Math 1992;1-272. https://doi.org/10.1137/1.9781611970999.
• 28. Broniec A, Chodak J. Application of EEG-signal to control simple electric device. Automatics 2009;13:1059-67.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).