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An EEG Brain-Computer Interface to Classify Motor Imagery Signals

  • Maria Karoline Andrade
  • Maí­ra Araújo de Santana
  • Giselle Moreno
  • Igor Oliveira
  • Jhonnatan Santos
  • Marcelo Cairrão Araújo Rodrigues
  • Wellington Pinheiro dos SantosEmail author
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Abstract

Considering the increase in life expectancy, people started to invest in technologies capable of improving the quality of life. One of these technologies is the Brain-Machine Interface. Combined with EEG signals, this technique may allow individuals with some motor disabilities to perform activities of daily living. Motor Imagery came up as an important tool to support this population. So they may send commands to external devices by using their brain voluntary activity. In this chapter, the performance of an Imagery EEG-based BCI engine was accessed by applying Wavelet transform to the signals and extracting metrics used to describe digital signals. We used signals from the motor imagery of the right hand, left hand and foot movements. Different intelligent classifiers were tested. We achieved results greater than 99% of accuracy and Kappa above 0.99. The method is promising and can be used for future evaluations with several individuals to verify reproducibility.

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Notes

Acknowledgments

We are grateful to the Brazilian research-funding agencies CAPES, CNPq and Facepe, for the partial support for this research.

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Maria Karoline Andrade
    • 1
  • Maí­ra Araújo de Santana
    • 1
  • Giselle Moreno
    • 1
  • Igor Oliveira
    • 1
  • Jhonnatan Santos
    • 1
  • Marcelo Cairrão Araújo Rodrigues
    • 1
  • Wellington Pinheiro dos Santos
    • 1
    Email author
  1. 1.Universidade Federal de PernambucoRecifeBrazil

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