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A Hybrid BCI for Gaming

  • Xing Song
  • Shane Xie
  • Wei Meng
Chapter

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of LeedsLeedsUnited Kingdom
  2. 2.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand
  3. 3.School of Information EngineeringWuhan University of TechnologyWuhanChina
  4. 4.The University of AucklandAucklandNew Zealand

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