International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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EEG Feature Classification Based on Grip Strength for BCI Applications

  • Kim, Dong-Eun (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Yu, Je-Hun (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (Department of Electrical and Electronics Engineering, Chung-Ang University)
  • Received : 2015.12.08
  • Accepted : 2015.12.24
  • Published : 2015.12.25
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Abstract

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.

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References

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