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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

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.

Keywords

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

References

  1. L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, and A. Pazos, "Automatic feature extraction using genetic programming: an application to epileptic EEG classification," Expert Systems with Applications, vol. 38, no. 8, pp. 10425-10436, 2011. http://dx.doi.org/10.1016/j.eswa.2011.02.118 https://doi.org/10.1016/j.eswa.2011.02.118
  2. J. D. R. Millan, F. Galan, D. Vanhooydonck, E. Lew, J. Philips, and M. Nuttin, "Asynchronous non-invasive brain-actuated control of an intelligent wheelchair," in Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2009), Minneapolis, MN, 2009, pp. 3361-3364. http://dx.doi.org/10.1109/IEMBS.2009.5332828 https://doi.org/10.1109/IEMBS.2009.5332828
  3. D. E. Kim, S. M. Park, and K. B. Sim, "Study on the correlation between grip strength and EEG," Journal of Institute of Control, Robotics and System, vol. 19, no. 9, pp. 853-859, 2013. http://dx.doi.org/10.5302/J.ICROS.2013.13.1916 https://doi.org/10.5302/J.ICROS.2013.13.1916
  4. D. E. Kim, T. J. Lee, S. M. Park, K. E. Ko, and K. B. Sim, "EEG analysis following change in hand grip force level for BCI based robot arm force control," Journal of Korean Institute of Intelligent Systems, vol. 23, no. 2, pp. 172-177, 2013. http://dx.doi.org/10.5391/JKIIS.2013.23.2.172 https://doi.org/10.5391/JKIIS.2013.23.2.172
  5. D. E. Kim, J. H. Yu, and K. B. Sim, "EEG feature classification for precise motion control of artificial hand," Journal of Korean Institute of Intelligent Systems, vol. 25, no. 1, pp. 29-34, 2015. http://dx.doi.org/10.5391/JKIIS.2015.25.1.029 https://doi.org/10.5391/JKIIS.2015.25.1.029
  6. S. I. Lee, "EEG and left/right directional analysis for brain-computer interface," M.S. thesis, Pusan National University, Busan, Korea, 2011.
  7. Y. Tang, J. Tang, and A. Gong, "Multi-class EEG classification for brain computer interface based on CSP," in Proceedings of International Conference on BioMedical Engineering and Informatics (BMEI2008), Sanya, China, 2008, pp. 469-472. http://dx.doi.org/10.1109/BMEI.2008.72 https://doi.org/10.1109/BMEI.2008.72
  8. M. Grosse-Wentrup and M. Buss, "Multiclass common spatial patterns and information theoretic feature extraction," IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1991-2000, 2008. http://dx.doi.org/10.1109/TBME.2008.921154 https://doi.org/10.1109/TBME.2008.921154
  9. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the 5th Annual Workshop on Computational Learning Theory, Pittsburgh, PA, 1992, pp. 144-152. http://dx.doi.org/10.1145/130385.130401 https://doi.org/10.1145/130385.130401

Acknowledgement

Supported by : NRF