Robotic Motion Control via P300-based Brain-Computer Interface System


  • Boning Li Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Jinsha Liu Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Jianting Cao RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan



Robot Control, P300, BCI, EEG


BCI have ignited extensive research interest in fields such as neuroscience, artificial intelligence, and biomedical engineering, as they offer an opportunity to interact directly with the external environment  brain signals. Despite the immense potential for applications, practical use of BCI still faces several challenges, including equipment cost and operational complexity. This study aims to develop a Brain-Computer Interface system based on P300 visual stimuli, utilizing a low-cost, user-friendly portable Muse EEG equipment for data acquisition. We designed and implemented a P300 visual stimulator in a 3x3 grid pattern, acquire the user's EEG signals using the Muse EEG equipment, and classify the data using a SVM classifier, ultimately realizing control over robot movement. Offline experimental results demonstrated an accuracy of 84.1% for the classifier under offline stage, while online stage achieved a successful execution rate of 81.2%. These findings substantiate the feasibility and potential of using low-cost, portable devices like the Muse EEG equipment for BCI research, opening new avenues in the field.


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[Allison et al.(2010)Allison, Brunner, Kaiser, Müller-Putz, Neuper, and Pfurtscheller] Brendan Z Allison, Clemens Brunner, Vera Kaiser, Gernot R Müller-Putz, Christa Neuper, and Gert Pfurtscheller. Toward a hybrid brain– computer interface based on imagined movement and visual attention. Journal of neural engineering, 7(2):026007, 2010. doi:

[Chen et al.(2015)Chen, Wang, Nakanishi, Gao, Jung, and Gao] Xiaogang Chen, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, and Shangkai Gao. High-speed spelling with a noninvasive brain– computer interface. Proceedings of the national academy of sciences, 112(44):E6058–E6067, 2015. doi:

[Gramann et al.(2011)Gramann, Gwin, Ferris, Oie, Jung, Lin, Liao, and Makeig] Klaus Gramann, Joseph T Gwin, aniel P Ferris, Kelvin Oie, Tzyy-Ping Jung, Chin-Teng Lin, Lun-De Liao, and Scott Makeig. Cognition in action: imaging brain/body dynamics in mobile humans. 2011. doi:

[Krusienski et al.(2006)Krusienski, Sellers, Cabestaing, Bayoudh, McFarland, Vaughan, and Wolpaw] Dean J Krusienski, Eric W Sellers, François Cabestaing, Sabri Bayoudh, Dennis J McFarland, Theresa M Vaughan, and onathan R Wolpaw. A comparison of classification techniques for the p300 speller. Journal of neural engineering, 3(4):299, 2006. doi:

[Lécuyer et al.(2008)Lécuyer, Lotte, Reilly, Leeb, Hirose, and Slater] Anatole Lécuyer, Fabien Lotte, Richard B Reilly, Robert Leeb, Michitaka Hirose, and Mel Slater. Brain-computer interfaces, virtual reality, and videogames. Computer, 41(10):66–72, 2008. doi:

[Lotte et al.(2018)Lotte, Bougrain, Cichocki, Clerc, Congedo, Rakotomamonjy, and Yger] Fabien Lotte, Laurent ougrain, Andrzej Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, and Florian Yger. A review of classification algorithms for eeg-based brain–computer interfaces: a 10 year update. Journal of neural engineering, 15(3):031005, 2018. doi: 10.1088/1741-2552/aab2f2.

[Mak and Wolpaw(2009)] Joseph N Mak and Jonathan R Wolpaw. Clinical applications of brain-computer inter-faces: current state and future prospects. IEEE reviews in biomedical engineering, 2:187–199, 2009. doi:

[Martens et al.(2009)Martens, Hill, Farquhar, and Schölkopf] SMM Martens, NJ Hill, J Farquhar, and B Schölkopf. Overlap and refractory effects in a brain–computer interface speller based on the visual p300 event-related potential. Journal of neural engineering, 6(2):026003, 2009. doi:

[Nicolas-Alonso and Gomez-Gil(2012)] Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil. Brain computer interfaces, a review. sensors, 12(2):1211–1279, 2012. doi:

[Pfurtscheller and Neuper(2001)] Gert Pfurtscheller and Christa Neuper. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7):1123–1134, 2001. doi:

[Polich(2007)] John Polich. Updating p300: an integrative theory of p3a and p3b. Clinical neurophysiology, 118(10): 2128–2148, 2007. doi:

[Ratti et al.(2017)Ratti, Waninger, Berka, Ruffini, and Verma] Elena Ratti, Shani Waninger, Chris Berka, Giulio Ruffini, and Ajay Verma. Comparison of medical and consumer wireless eeg systems for use in clinical tri- als. Frontiers in human neuroscience, 11:398, 2017. doi:

[Wolpaw and Wolpaw(2012)] Jonathan Wolpaw and Elizabeth Winter Wolpaw. Brain–Computer Interfaces: Principles and Practice. Oxford University Press, 01 2012. ISBN 9780195388855. doi: 10.1093/acprof:oso/9780195388855.001. 0001. doi:




How to Cite

Li, B., Liu, J., & Cao, J. (2023). Robotic Motion Control via P300-based Brain-Computer Interface System. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 23, 105–115.



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