Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter
DOI:
https://doi.org/10.24297/ijct.v23i.9485Keywords:
Support vector machine, P300, Brain-Controlled System, wireless Digital-to-Analog converterAbstract
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. https://doi.org/https://doi.org/10.1016/S0277-9536(98)00441-9
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. https://doi.org/https://doi.org/10.1016/j.jneumeth.2014.03.011
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. https://doi.org/https://doi.org/10.1109/WEROB.2017.8383877
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. https://doi.org/https://doi.org/10.1007/978-3-540-88906-9_42
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. https://doi.org/https://doi.org/10.3389/fnins.2019.01243
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. https://doi.org/https://doi.org/10.1186/1743-0003-11-7
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. https://doi.org/https://doi.org/10.3390/electronics8121387
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. https://doi.org/https://doi.org/10.1109/TBME.2013.2270283
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. https://doi.org/https://doi.org/10.24466/ijbschs.26.2_63
Luschas, S., Schreier, R., & Lee, H.-S. (2004). Radio frequency digital-to-analog converter. IEEE Journal of Solid-State Circuits, 39 (9), 1462–1467. https://doi.org/https://doi.org/10.1109/JSSC.2004.829377
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. https://doi.org/https://doi.org/10.21275/5011802
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.
https://doi.org/https://doi.org/10.1109/IEMBS.2008.4649238
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. https://doi.org/https://doi.org/10.1007/978-3-642-15314-3_32
Suthaharan, S. (2015). Machine learning models and algorithms for big data classification: Thinking with examples for effective learning (Vol. 36). Springer. https://doi.org/https://doi.org/10.1007/978-1-4899-7641-3
Tang, J., Liu, Y., Hu, D., & Zhou, Z. (2018). Towards bci-actuated smart wheelchair system. Biomedical engineering online, 17 (1), 1–22. https://doi.org/https://doi.org/10.1186/s12938-018-0545-x
Van de Plassche, R. J. (2013). Cmos integrated analog-to-digital and digital-to-analog converters (Vol. 742). Springer Science & Business Media. https://doi.org/https://doi.org/10.1007/978-1-4757-3768-4
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. https://doi.org/https://doi.org/10.1016/j.procs.2018.01.079
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. https://doi.org/https://doi.org/10.1109/ACIRS.2017.7986083
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. https://doi.org/https://doi.org/10.1109/TNSRE.2017.2766365
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.
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Copyright (c) 2023 Zizhu Li, Boning Li, Wenping Luo, Jianting Cao
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