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A Hybrid BCI for Gaming

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Biomechatronics in Medical Rehabilitation

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Abstract

As interfaces between the brain and computers, EEG-based BCIs are useful to the control of assistive devices and technologies. This chapter proposes a hybrid EEG-based BCI for controlling a video game using EEG rhythms and SSVEPs. It builds on the work presented in prior chapters, applying it to gaming that involves training aspects and more complex commands. An EEG cap with seven active electrodes was used to collect users’ brain signals. The signals were passed through noise suppression and classification using the Fast Fourier Transform (FFT), the ANBF and adaptive thresholds.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Leeds, Leeds, United Kingdom

    Shane Xie

  2. Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand

    Shane Xie & Wei Meng

  3. School of Information Engineering, Wuhan University of Technology, Wuhan, China

    Wei Meng

  4. The University of Auckland, Auckland, New Zealand

    Xing Song

Authors
  1. Xing Song
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  2. Shane Xie
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  3. Wei Meng
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Corresponding author

Correspondence to Shane Xie .

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Leeds, Leeds, United Kingdom

    Shane (S.Q.) Xie

  2. School of Information Engineering, Wuhan University of Technology, Wuhan, Switzerland

    Wei Meng

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Song, X., Xie, S., Meng, W. (2017). A Hybrid BCI for Gaming. In: Xie, S., Meng, W. (eds) Biomechatronics in Medical Rehabilitation. Springer, Cham. https://doi.org/10.1007/978-3-319-52884-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-52884-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52883-0

  • Online ISBN: 978-3-319-52884-7

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