Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter


  • Zizhu Li Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan
  • Boning Li School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
  • Wenping Luo RIKEN Center for Advanced Intelligence Project(AIP), 103-0027, Japan
  • Jianting Cao Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan



Support vector machine, P300, Brain-Controlled System, wireless Digital-to-Analog converter


This article presents a P300 brain-controlled wheelchair system utilizing a wireless Digital-to-Analog converter for signal transmission. The wireless Digital-to-Analog converter addresses issues with device connectivity and simplifies signal transmission, removing the need for complex serial port protocols. A support vector machine model is trained to extract the P300 component from the Electroencephalogram signal. A P300 stimulator is designed to elicit the P300 component response, with subjects controlling the wheelchair's movement by looking at randomly flickering white circles. Experimental validation is conducted on a modified wheelchair, demonstrating the effectiveness and reliability of the proposed method.


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Bickenbach, J. E., Chatterji, S., Badley, E. M., & Üstün, T. B. (1999). Models of disablement, universalism and the international classification of impairments, disabilities and handicaps. Social science & medicine, 48 (9), 1173–1187.

Cao, L., Li, J., Ji, H., & Jiang, C. (2014). A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control. Journal of neuroscience methods, 229, 33–43.

Carrasquilla-Batista, A., Quirós-Espinoza, K., & Gómez-Carrasquilla, C. (2017). An internet of things (iot) application to control a wheelchair through eeg signal processing. 2017 International Symposium on Wearable Robotics and Rehabilitation (WeRob), 1–1.

Choi, K., & Cichocki, A. (2008). Control of a wheelchair by motor imagery in real time. Intelligent Data Engineering and Automated Learning–IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5,2008 Proceedings 9, 330–337.

Huang, Q., Zhang, Z., Yu, T., He, S., & Li, Y. (2019). An eeg-/eog-based hybrid brain-computer interface: Application on controlling an integrated wheelchair robotic arm system. Frontiers in neuroscience, 13, 1243.

Kaufmann, T., Herweg, A., & Kübler, A. (2014). Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials. Journal of neuroengineering and rehabilitation, 11, 1–17.

Korovesis, N., Kandris, D., Koulouras, G., & Alexandridis, A. (2019). Robot motion control via an eeg-based brain–computer interface by using neural networks and alpha brainwaves. Electronics, 8 (12), 1387.

Li, Y., Pan, J., Wang, F., & Yu, Z. (2013). A hybrid bci system combining p300 and ssvep and its application to wheelchair control. IEEE Transactions on Biomedical Engineering, 60 (11), 3156–3166.

LUO, W., CAO, J., ISHIKAWA, K., & JU, D. (2021). Experimental validation of intelligent recognition of eye movements in the application of autonomous vehicle driving. International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association, 26 (2), 63–72.

Luschas, S., Schreier, R., & Lee, H.-S. (2004). Radio frequency digital-to-analog converter. IEEE Journal of Solid-State Circuits, 39 (9), 1462–1467.

Mahmoud, A., Hamoud, M., Ahmad, A. M., & Ahmad, A. S. (2018). Controlling a wheelchair using human-computer interaction. Int. J. Sci. Res, 7, 681–686.

Pires, G., Castelo-Branco, M., & Nunes, U. (2008). Visual p300-based bci to steer a wheelchair: A bayesian approach.2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 658–661.

Stamps, K., & Hamam, Y. (2010). Towards inexpensive bci control for wheelchair navigation in the enabled environment–a hardware survey. Brain Informatics: International Conference, BI 2010, Toronto, ON, Canada, August 28-30, 2010. Proceedings, 336–345.

Suthaharan, S. (2015). Machine learning models and algorithms for big data classification: Thinking with examples for effective learning (Vol. 36). Springer.

Tang, J., Liu, Y., Hu, D., & Zhou, Z. (2018). Towards bci-actuated smart wheelchair system. Biomedical engineering online, 17 (1), 1–22.

Van de Plassche, R. J. (2013). Cmos integrated analog-to-digital and digital-to-analog converters (Vol. 742). Springer Science & Business Media.

Voznenko, T. I., Chepin, E. V., & Urvanov, G. A. (2018). The control system based on extended bci for a robotic wheelchair. Procedia computer science, 123, 522–527.

Wang, D., & Yu, H. (2017). Development of the control system of a voice-operated wheelchair with multi-posture characteristics. 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 151–155.

Yu, Y., Zhou, Z., Liu, Y., Jiang, J., Yin, E., Zhang, N., Wang, Z., Liu, Y., Wu, X., & Hu, D. (2017). Self-paced operation of a wheelchair based on a hybrid brain-computer interface combining motor imagery and p300 potential. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (12), 2516–2526.

Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., & Gu, Z. (2015). Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE transactions on neural systems and rehabilitation engineering, 24 (1), 128–139.




How to Cite

Li, Z., Li, B., Luo, W., & Cao, J. (2023). Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 23, 93–104.



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