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Humanoid Robot Control System Based on AR-SSVEP

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Published:20 August 2020Publication History

ABSTRACT

In the brain computer interface (BCI), steady-state visual evoked potential (SSVEP) is a relatively common input signal of human-computer interaction systems. However, it often requires a fixed computer screen as a visual stimulator, which limits the flexibility of its application. In this research, HoloLens glasses are used as visual stimulators in a BCI system based on augmented reality to control the humanoid robot NAO to recognize and grasp objects. The system uses augmented reality device to induce steady-state visual evoked potential. The user does not need to perform visual stimulation at a fixed position, which can enhance the applicability in complex environments, thereby achieving more natural human-computer interaction. In order to achieve grasping, this study uses robot monocular vision recognition and establish forward and inverse kinematics models of the robot arms. EEG experiments have been performed to verify the accuracy of the system, it is more flexible and convenient for using augmented reality as stimulators in a humanoid robot control system based on SSVEP-BCI.

References

  1. Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., Mcfarland, D. J., & Peckham, P. H.. 2000. Brain -- computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering.Google ScholarGoogle ScholarCross RefCross Ref
  2. Wolpaw, J.R., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, OxfordGoogle ScholarGoogle Scholar
  3. D. Y. Zhi, X. L. Du, J. Zhao, Z. P. Wu, and W Li.. 2016. Brain-robot interaction system based on portable brain signal collector. Journal of Electronic measurement and Instrumentation, vol. 30, no. 5, pp. 694--701.Google ScholarGoogle Scholar
  4. Somashekhar S Hiremath, Robins Mathew, and Jennifer Jacob, "Implementation of Low Cost Vision Based Measurement System: Motion Analysis of Indoor Robot," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 6, pp. 575--582, November 2018.Google ScholarGoogle Scholar
  5. Davood Pour Yousefian Barfeh, Patrice Xandria Mari A. Delos Reyes, and Myrna A. Coliat, "Real-Time Multi Target Capturing Using Partitioning in Robot Vision" International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 1, pp. 117--121, January 2020.Google ScholarGoogle Scholar
  6. Zhai, R., Wen, S., Zhu, J., & Guo, G.. 2018. Trajectory planning of NAO robot arm based on target recognition. International Conference on Advanced Mechatronic Systems. IEEE.Google ScholarGoogle Scholar
  7. Wei, L., Mengfan, L., & Jing, Z.. 2015. Control of humanoid robot via motion-onset visual evoked potentials. Frontiers in Systems Neuroscience, 8.Google ScholarGoogle Scholar
  8. Spataro, R., Sorbello, R., Tramonte, S., Tumminello, G., Giardina, M., & Chella, A., et al. 2015. Reaching and grasping a glass of water by locked-in als patients through a bci-controlled humanoid robot. Journal of the Neurological Sciences, 357, e48--e49.Google ScholarGoogle ScholarCross RefCross Ref
  9. Sandra Mara Torres Müller .... 2013. Proposal of a ssvep-bci to command a robotic wheelchair. Journal of Control, Automation and Electrical Systems, 24(1-2), 97--105.Google ScholarGoogle ScholarCross RefCross Ref
  10. Chen, X., Zhao, B., Wang, Y., Xu, S., & Gao, X.. 2018. Control of a 7-dof robotic arm system with an ssvep-based bci. International Journal of Neural Systems, S0129065718500181.Google ScholarGoogle Scholar
  11. Mao, X., Li, W., Lei, C., Jin, J., Duan, F., & Chen, S.. 2019. A brain robot interaction system by fusing human and machine intelligence. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1--1.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kansaku, K., Hata, N., & Takano, K.. 2009. My thoughts through a robot's eyes: an augmented reality-brain - machine interface. Neuroscience Research, 66(2), 219--222.Google ScholarGoogle ScholarCross RefCross Ref
  13. Horii, S., Nakauchi, S., & Kitazaki, M.. 2015. AR-SSVEP for brain-machine interface: Estimating user's gaze in head-mounted display with USB camera. 2015 IEEE Virtual Reality (VR). IEEE.Google ScholarGoogle Scholar
  14. Faller, J., Allison, B. Z., Brunner, C., Scherer, R., Schmalstieg, D., & Pfurtscheller, G., et al. 2017. A feasibility study on ssvep-based interaction with motivating and immersive virtual and augmented reality.Google ScholarGoogle Scholar
  15. Lixin, Z., Yukun, Z., Yufeng, K., Jiale, D., Minpeng, X., & Dong, M.. 2019. A study of argument reality based brain-computer interface (ar-bci) in hololens. Chinese Journal of Biomedical Engineering.Google ScholarGoogle Scholar
  16. Chen, X., Wang, Y., Gao, S., Jung, T. P., & Gao, X.. 2015. Filter bank canonical correlation analysis for implementing a high-speed ssvep-based brain-computer interface. Journal of neural engineering, 12(4), 046008.1--046008.14.Google ScholarGoogle ScholarCross RefCross Ref
  17. Chen, X., Chen, Z., Gao, S., & Gao, X.. 2014. A high-itr ssvep-based bci speller. Brain-Computer Interfaces, 1(3-4), 181--191.Google ScholarGoogle ScholarCross RefCross Ref

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      cover image ACM Other conferences
      ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence
      April 2020
      563 pages
      ISBN:9781450377089
      DOI:10.1145/3404555

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      Publication History

      • Published: 20 August 2020

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