Skip to main content

Exploration of a Hybrid Design Based on EEG and Eye Movement

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10462))

Included in the following conference series:

  • 5805 Accesses

Abstract

This study presents a novel hybrid interface based on both electroencephalography (EEG) and eye movement. The detection of combination EEG with eye movement provides a new means of communication for patients whose muscular damage are unable to communicate. And this method can translate some brain responses into actions. In this paper, based on the motor imagery, event related synchronization/desychronization (ERS/ERD) were tested by using time-frequency spectrum and brain topographic mapping. A features extraction algorithm is proposed based on common spatial pattern (CSP), then the support vector machine (SVM) were carried out to classificate data. An EEG recording device integrated with an eye tracker can be complementary to attain improved performance and a better efficiency. The eye movement signals (via eye tracker of Tobbi) and EEG signals of ERS/ERD are as the input of hybrid BCI system simultaneously while subjects follow movement of the arrows in each direction. The recognition accuracy of the entire system reaches to 86.1%. The results showed that the proposed method was efficient in the classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Wolpaw, J.R., McFarland, D.J.: An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78, 252–259 (1991)

    Article  Google Scholar 

  2. Schalk, G.D., McFarland, J.T.: BCI2000 a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)

    Article  Google Scholar 

  3. McFarland, D.J., Wolpaw, J.R.: Brain-computer interface for communication and control. Commun. ACM 54, 60–66 (2002)

    Article  Google Scholar 

  4. Wolpaw, J.R.: BCI meeting 2005-workshop on signals and recording methods. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 138–141 (2006)

    Article  Google Scholar 

  5. Pfurtscheller, G.: The hybrid BCI. Front. Neurosci. 4(30), 1–11 (2010)

    Google Scholar 

  6. Allision, BZ.: Extending BCIs through hybridization and intelligent control. J. Neural Eng. 9 (2012)

    Google Scholar 

  7. Millan, J.R.: Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci. 4, 161 (2010)

    Google Scholar 

  8. Wechsler, Q.H., Duchowski, A.T.: Special issue: eye detection and tracking. Comput. Vis. Image Underst. 98, 1–3 (2005)

    Article  Google Scholar 

  9. Kawato, T.: Detection and tracking of eyes for gaze camera control. Image Vis. Comput. 22, 1031–1038 (2004)

    Article  Google Scholar 

  10. Kim, J.: A simple pupil-independent method for recording eye movements in rodents using video. J. Neurosci. Methods 138, 165–171 (2004)

    Article  Google Scholar 

  11. Vertegaal, R.: A Fitts law comparison of eye tracking and manual input in the selection of visual targets. In: International Conference on Multimodal Interact (ICMI), pp. 241–248 (2008)

    Google Scholar 

  12. Blankertz, B., Kawanabe, M.: Invariant common spatial patterns: alleviating nonstationarities in brain-computer interfacing. In: Advances in Neural Information Processing Systems, Cambridge, Canada, vol. 20, pp. 113–120 (2008)

    Google Scholar 

  13. Koike, Y.: A real-time BCI with a small number of channels based on CSP. Neural Comput. Appl. 20, 1187–1192 (2011)

    Article  Google Scholar 

  14. Ang, K.K., Chin, Z.Y., Zhang, H.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: 2008 IEEE International Joint Conference on Neural Networks, Hong Kong, pp. 2390–2397 (2008)

    Google Scholar 

  15. Hayashi, Y., Kiguchi, K.: A study of features of EEG signals during upper-limb motion. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, pp. 943–946 (2015)

    Google Scholar 

  16. Waldert, S., Demandt, E.: Hand movement direction decoded from MEG and EEG. J. Neurosci. 28(4), 1000–1008 (2008)

    Article  Google Scholar 

  17. Acl, B., Mohebbi, M.: Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med. 44(1), 51–64 (2008)

    Article  Google Scholar 

  18. Lanez, E.: Mental tasks selection method for a SVM-based BCI system. In: 2013 IEEE International Systems Conference (SysCon), Orlando, FL, pp. 767–771 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan Hao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Yang, J., Hao, Y., Bai, D., Jiang, Y., Yokoi, H. (2017). Exploration of a Hybrid Design Based on EEG and Eye Movement. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65289-4_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65288-7

  • Online ISBN: 978-3-319-65289-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics