EEG Feature Classification Based on Grip Strength for BCI Applications

Kim, Dong-Eun;Yu, Je-Hun;Sim, Kwee-Bo

  • Received : 2015.12.08
  • Accepted : 2015.12.24
  • Published : 2015.12.25


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.


Brain-computer interface;Electroencephalogram;Multi-common spatial pattern


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Supported by : NRF