Brain Operated Wheelchair Using a Single Electrode EEG Device and BCI
Abstract
This paper predominantly explains the use of a simplistic uni-polar device to obtain EEG for the development of a Brain-Computer Interface (BCI). In contrast, BCI's eye-blinking stimuli can also be obtained. Consequently, focus and eye-blinking stimuli can be captured as control pulses in electric wheelchairs via a computer interface and electrical interface. This survey paper aims to provide a feasible solution to integrate a Brain-Computer Interface (BCI) with automated identification and avoidance of obstacles. The automated obstacle detection and avoidance system aims to provide a way to easily detect obstacles and easily correct the course.
Downloads
References
C. K. Huang, Z. W. Wang, G. W. Chen, and C. Y. Yang, “Development of a smart wheelchair with dual functions: Real-time control and automated guide,” in 2017 2nd International Conference on Control and Robotics Engineering (ICCRE), IEEE, pp. 73-76, April 2017.
I. Y. Panessai and A. S. Abdulbaqi, “An Efficient Method of EEG Signal Compression and Transmission Based Telemedicine”, Journal of Theoretical and Applied Informtion Technology, vol. 97, no. 4, pp. 1060-1070, 2019.
T. Carlson and Y. Demiris, “Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload,” Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), IEEE, vol.42, no. 3, pp. 876-888, 2012.
J. S. Lin, K. C. Chen, and W. C. Yang, “EEG and eye-blinking signals through a Brain-Computer Interface based control for electric wheelchairs with wireless scheme,” in 4th International Conference on New Trends in Information Science and Service Science, IEEE, pp. 731-734, May. 2010.
Y. Zhang, X. Xu, H. Lu, and Y. Dai, “Two-stage obstacle detection based on stereo vision in unstructured environment,” in 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, IEEE, Vol. 1, pp. 168-172, August 2014.
G. Reshmi and A. Amal, “Design of a BCI system for piloting a wheelchair using five class MI Based EEG,” in 2013 Third International Conference on Advances in Computing and Communications”, IEEE, pp. 25-28, August 2013.
S. K. Swee and L. Z. You, “Fast Fourier analysis and EEG classification brainwave-controlled wheelchair,” in 2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE), IEEE, pp. 20-23, July 2016.
R. Zhang, Y. Li, Y. Yan, H. Zhang, S. Wu, T. Yu, and Z. Gu, “Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation,” Transactions on neural systems and rehabilitation engineering, IEEE, vol. 24, no. 1, pp. 128-139, 2015.
Z. Su, X. Xu, J. Ding, and W. Lu, “Intelligent wheelchair control system based on BCI and the image display of EEG,” in 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), IEEE, pp. 1350-1354, October 2016.
P. Lahane, S. P. Adavadkar, S. V. Tendulkar, B. V. Shah, and S. Singhal, “Innovative Approach to Control Wheelchair for Disabled People Using BCI,” in 2018 3rd International Conference for Convergence in Technology (I2CT), IEEE, pp. 1-5, April 2018.
I. A. Mirza, A. Tripathy, S. Chopra, M. D'Sa, K. Rajagopalan, A. D'Souza, and N. Sharma, “Mind-controlled wheelchair using an EEG headset and arduino microcontroller,” in 2015 International Conference on Technologies for Sustainable Development (ICTSD), IEEE, pp. 1-5, February 2015.