Classification Procedure for Motor Imagery EEG Data

  • Ellton Sales BarrosEmail author
  • Nelson Neto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10915)


Brain computer interface establishes a new model of communication, whereby it is possible to communicate using only cerebral signals, that can be obtained from different kind of cerebral stimuli. By the way, one of the most common stimulus is the motor imagery of the arms. However, since a set of variables leads to different levels of classification accuracy, it is necessary to search for procedures that can enhance the recognition accuracy of brain signals in order to create more precise systems. This paper proposes a classification procedure for discrimination of two motor imagery classes obtained using the Emotiv EPOC+ EEG signal acquisition device. The Emotiv EPOC+ has 14 input channels, but only four were used – the ones directly related with the capture of motor imagery signals. The presented procedure was created considering the MI common spatial pattern package from the OpenVibe software and the support vector machine (SVM) classification approach. As well, the procedure runs under the OpenVibe scenarios. A database with motor imagery signals from five subjects was built in order to perform the classification tests. In order to select the best features, several aspects from the signal acquisition until the classification process were analysed, such as selection of the best Kernel to SVM classifier, frequency band, filter output channels, and a grid-search to estimate the classifier parameters. At the end, an increase of 28,96% in the mean accuracy was achieved, regarding to the OpenVibe MI standard scenario.


Brain computer interface Support vector machine Motor imagery 


  1. 1.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8(2), 164–173 (2000)CrossRefGoogle Scholar
  2. 2.
    Djemal, R., Bazyed, A.G., Belwafi, K., Gannouni, S., Kaaniche, W.: Three-class EEG-based motor imagery classification using phase-space reconstruction technique. Brain Sci. 6(3), 36 (2016)CrossRefGoogle Scholar
  3. 3.
    Abdalsalam, M.E., Yusoff, M.Z., Kamel, N., Malik, A., Meselhy, M.: Mental task motor imagery classifications for noninvasive brain computer interface. In: Intelligent and Advanced Systems, ICIAS, Kuala Lumpur, pp. 1–5. IEEE (2014)Google Scholar
  4. 4.
    Ma, Y., Ding, X., She, Q., Luo, Z., Potter, T., Zhang, Y.: Classification of motor imagery EEG signals with support vector machines and particle swarm optimization. Comput. Math. Methods Med. 2016, 8 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Yang, Y., Kyrgyzov, O., Wiart, J., Bloch, I.: Subject-specific channel selection for classification of motor imagery electroencephalographic data. In: Acoustics, Speech and Signal Processing, ICASSP, Vancouver, pp. 1277–1280. IEEE (2013)Google Scholar
  6. 6.
    Sivakami, A., Devi, S.S.: Analysis of EEG for motor imagery based classification of hand activities. Int. J. Biomed. Eng. Sci. 2(3), 11–22 (2015)Google Scholar
  7. 7.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Da Silva, F.L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  8. 8.
    Pfurtscheller, G., Da Silva, F.L.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)CrossRefGoogle Scholar
  9. 9.
    OpenVibe. Homepage. Accessed 23 Jan 2018
  10. 10.
    Jiralerspong, T., Liu, C., Ishikawa, J.: Identification of three mental states using a motor imagery based brain machine interface. In: Computational Intelligence in Brain Computer Interfaces, CIBCI, Orlando, pp. 49–56. IEEE (2015)Google Scholar
  11. 11.
    Hurtado-Rincon, J., Rojas-Jaramillo, S., Ricardo-Cespedes, Y., Alvarez-Meza, A.M., Castellanos-Dominguez, G.: Motor imagery classification using feature relevance analysis: an Emotiv-based BCI system. In: Image, Signal Processing and Artificial vision, STSIVA, Armenia, pp. 1–5. IEEE (2014)Google Scholar
  12. 12.
  13. 13.
    Wolpaw, J.R., Boulay, C.B.: Brain signals for brain–computer interfaces. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces. The Frontiers Collection, pp. 29–46. Springer, Heidelberg (2009). Scholar
  14. 14.
    Szachewicz, P.: Classification of motor imagery for braincomputer interfaces. Poznan University of Technology, Institute of Computing Science, Poznań (2013)Google Scholar
  15. 15.
    Herman, P., Prasad, G., McGinnity, T.M., Coyle, D.: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 16(4), 317–326 (2008)CrossRefGoogle Scholar
  16. 16.
    Carrera-Leon, O., Ramirez, J.M., Alarcon-Aquino, V., Baker, M., D’Croz-Baron, D., Gomez-Gil, P.: A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction. In: Engineering Applications Workshop, WEA, Bogota, pp. 1–6. IEEE (2012)Google Scholar
  17. 17.
    Vargic, R., Chlebo, M., Kacur, J.: Human computer interaction using BCI based on sensorimotor rhythm. In: Intelligent Engineering Systems, INES, Bratislava, pp. 91–95. IEEE (2015)Google Scholar
  18. 18.
    Mathur, A., Foody, G.M.: Multiclass and binary SVM classification: implications for training and classification users. IEEE Geosci. Remote Sens. Lett. 5(2), 241–245 (2008)CrossRefGoogle Scholar
  19. 19.
    Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig. Process. Mag. 25(1), 41–56 (2008)CrossRefGoogle Scholar
  20. 20.
    Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)CrossRefGoogle Scholar
  21. 21.
    Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFederal University of ParáBelémBrazil

Personalised recommendations