Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG

@article{Wang2015DevelopingAO,
  title={Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG},
  author={Yu-Te Wang and M. Nakanishi and S. L. Kappel and P. Kidmose and D. Mandic and Yijun Wang and C. Cheng and T. Jung},
  journal={2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2015},
  pages={2271-2274}
}
  • Yu-Te Wang, M. Nakanishi, +5 authors T. Jung
  • Published 2015
  • Computer Science, Medicine
  • 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
    • The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the… CONTINUE READING
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    11 Citations
    Background Citations
    3
    Methods Citations
    4
    11 Citations
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    References

    SHOWING 1-10 OF 16 REFERENCES
    An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.
    • 571
    Enhancing detection of steady-state visual evoked potentials using individual training data
    • Yijun Wang, M. Nakanishi, Yu-Te Wang, Tzyy-Ping Jung
    • Computer Science, Medicine
    • 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    • 2014
    • 36
    • PDF
    Brain-Computer Interfaces Based on Visual Evoked Potentials
    • 314
    • PDF
    A High-Speed Brain Speller using steady-State Visual evoked potentials
    • 180
    • PDF
    Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.
    • 204
    A Study of Evoked Potentials From Ear-EEG
    • 99
    • PDF
    Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller
    • 40
    • PDF
    Generating Visual Flickers for Eliciting Robust Steady-State Visual Evoked Potentials at Flexible Frequencies Using Monitor Refresh Rate
    • 64
    • PDF
    A practical VEP-based brain-computer interface.
    • Yijun Wang, R. Wang, X. Gao, Bo Hong, S. Gao
    • Computer Science, Medicine
    • IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    • 2006
    • 359
    • PDF
    Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
    • 205
    • PDF