Brainwave Controlled Wheelchair (BCW)



EOI: 10.11242/viva-tech.01.03.02

Download Full Text here



Citation

Rubini Pulliadi, Suchan Khade, Jiteshkumar Yadav, Nutan Malekar, "Brainwave Controlled Wheelchair (BCW)", VIVA-IJRI Volume 1, Issue 3, Article 2, pp. 1-5, 2020. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The locomotive disabled people and elderly people cannot control the wheelchair manually. The key objective of this paper is to help the locomotive disabled and old people to easily manoeuvre without any social aid through a brainwave-controlled wheelchair. There are various types of wheelchair available in the market such as Voice controlled wheelchair, Joystick control wheelchair, Smart phone controlled wheelchair, Eye controlled wheelchair, Mechanical wheelchair. These wheelchairs hold certain limitations for e.g. if the user is dumb; user cannot access voice controlled wheelchair, etc. Brain-computer interface (BCI) is a new method used to interface between the human mind and a digital signal processor. An Electroencephalogram (EEG) based BCI is connected with an artificial reality system to control the movement and direction of a wheelchair. This paperproposes brainwave controlled wheelchair, which uses the captured EEG signals from the brain. This EEG signals are then passed to Arduino. It converts into control signals which will in turn help to move the wheelchair in different direction.

Keywords

Brain Computer Interface (BCI), Locomotive disabled Persons, Mobility, Mind-link Electroencephalogram (EEG) sensor.

References

  1. Trinayan Saharia, Jyotika Bauri and Mrs. Chayanika Bhagabati, "Joystick Controlled Wheelchair", International Research Journal of Engineering and Technology (IRJET) Vol. 4, 2017, pp. 235-237.
  2. Nutthanan Wanluk, Sarinporn Visitsattapongse, Aniwat Juhong and C. Pintavirooj, "Smart Wheelchair Based on Eye Tracking", 9th Biomedical Engineering International Conference (BME- iCON), Laung Prabang, Laos, 2016, pp. 1-3.
  3. Mir Mohammad Tahsin, Rahat Khan, Ashoke Kumar Sen Gupta, "Assistive technology for physically challenged or paralyzed person using voluntary tongue movement", 5th International Conference on Informatics, Electronics and Vision (ICIEV), Chittagong, Bangladesh, 2016, pp. 293-296.
  4. Muhammad Tahir and Syed Ashfaque Ahmed, "Voice Controlled Wheelchair Using DSK TMS320C6711", International Conference on Signal Acquisition and Processing, 2009, pp. 217- 220.
  5. R. Posada-Gomez, L. H. Sainchez-Medel, G. Alor Hernandez, A. Martinez-Sibaja, A. Aguilar Laserrel. L. Leija-Salas, "A Hands Gesture System of Control for an Intelligent Wheelchair", 4th International Conference on Electrical and Electronics Engineering (ICEEE), Mexico City, Mexico, 2007, pp. 68-71.
  6. Djoko Purwanto, Ronny Mardiyanto and Kohei Arai, "Electric wheelchair control with gaze direction and eye blinking", 14th International Symposium on Artificial Life and Robotics, Oita, Japan, 2009, pp. 397-400.
  7. Mir Mohammad Tahsin, Rahat Khan, Ashoke Kumar Sen Gupta, "Assistive technology for physically challenged or paralyzed person using voluntary tongue movement", 5th International Conference on Informatics, Electronics and Vision (ICIEV), Chittagong, Bangladesh, 2016, pp. 293-296.
  8. Sim Kok Swee and Lim Zheng You, "Fast Fourier Analysis and EEG Classification Brainwave Controlled Wheelchair", 2nd International Conference on Control Science and Systems Engineering, Malaysia, 2016, pp. 20-22.
  9. Bright, D., Nair, A., Salvekar, D., Bhisikar, S. (2016). “EEG-based brain controlled prosthetic arm” 2016 Conference on Advances in Signal Processing (CASP).doi:10.1109/casp.2016.7746219.
  10. Brain Computer Interfaces, a Review, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304110/29
  11. Luzheng Bi, Xin-An Fan, Yili Liu, "EEG-Based Brain- Controlled Mobile Robots: A Survey ", IEEE transaction on human machine systems", vol. 43, March 2013, pp. 161-176.
  12. Wenchuan Jia, Dandan Huang, Xin Luo, Huayan Pu, Xuedong Chen, and Ou Bai, "Electroencephalography(EEG)-Based Instinctive BrainControl of a Quadruped Locomotion Robot", International Conference of the IEEE Engineering in Medicine and Biology Society, September 2012, pp.1777-1781.
  13. Schalk , G, McFarland, D. J.., Hinterberger, T., Birbauner, N., Wolpaw, J.R. (2004). BCI2000: A Genereal Purpose Brain-computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering, 51(6), 1034-1043. Doi:10.1109/tbme2004.827072
  14. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Electroenceph. Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791, June 2002.
  15. Anwar, D. Gupta, A. Naik, V. Sharma, S. K. (2017). Detecting meditation using a dry monoelectrode EEG sensor. 2017 9th International Conference on Communication Systems and Networks (COMSNETS).doi:10.1109/comsnets.2017.7945444.
  16. B. I. Morshed, and A. Khan, "A brief Review of Brain Signal Monitoring Technologies for BCI Applications: challenges and Prospects", Journal of Bioengineering and Biomedical Science, vol. 4, no. 1, pp. 1-10, May 2014.
  17. https://www.arduino.cc/
  18. https://www.mind-your-reality.com/brain_waves.html