A Method Based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI

  • Ziqing Xia
  • Likun XiaEmail author
  • Ming Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


The Common Spatial Pattern (CSP) algorithm is capable of solving the binary classification problem for the motor image task brain-computer interface (BCI). This paper proposes a novel method based on the Filter Bank Common Spatial Pattern (FBCSP) termed the Multiscale and Overlapping FBCSP (MO-FBCSP). We extend the CSP algorithm for multiclass by using the one-versus-one (OvO) strategy. Multiple periods are selected and combined with the overlapping spectrum of the filter bank which contains useful information. This method is evaluated on the benchmark BCI Competition IV dataset 2a with 9 subjects. An average accuracy of 80% was achieved with the random forest (RF) classifier, and the corresponding kappa value was 0.734. Quantitative results have shown that the proposed scheme outperforms the classical FBCSP algorithm by over 12%.


Brain-computer interface Motor imagery Machine learning EEG 



This work was partially supported by a Natural Science Foundation of China (NSFC) grant (61572076) and Beijing Advanced Innovation Center for Imaging Technology.


  1. 1.
    Zhang, Y., Wang, Y., Zhou, G., et al.: Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst. Appl. 96, 302–310 (2018)CrossRefGoogle Scholar
  2. 2.
    Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019)CrossRefGoogle Scholar
  3. 3.
    Dornhege, G., Blankertz, B., Curio, G., Muller, K.R.: Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans. Biomed. Eng. 51(6), 993–1002 (2004)CrossRefGoogle Scholar
  4. 4.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Silva, F.H.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  5. 5.
    Koles, Z.J., Lazar, M.S., Zhou, S.Z.: Spatial patterns underlying population differences in the background EEG. Brain Topogr. 2(4), 275–284 (1990)CrossRefGoogle Scholar
  6. 6.
    Lemm, S., Blankertz, B., Curio, G., et al.: Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans. Biomed. Eng. 52(9), 1541–1548 (2005)CrossRefGoogle Scholar
  7. 7.
    Dornhege, G., Blankertz, B., Krauledat, M., et al.: Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans. Biomed. Eng. 53(11), 2274–2281 (2006)CrossRefGoogle Scholar
  8. 8.
    Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition IV Datasets 2a and 2b. Front. Neurosci. 6, 39.
  9. 9.
    Iacoviello, D., Petracca, A., Spezialetti, M., et al.: A classification algorithm for electroencephalography signals by self-induced emotional stimuli. IEEE Trans. Cybern. 46(12), 3171–3180 (2016)CrossRefGoogle Scholar
  10. 10.
    Lu, N., Li, T., Ren, X., et al.: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans. Neural Syst. Rehabil. Eng. 25(6), 566–576 (2017)CrossRefGoogle Scholar
  11. 11.
    Sakhavi, S., Guan, G., Yan, S.C.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5619–5629 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Amin, S.U., Alsulaiman, M., Muhammad, G., et al.: Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification. IEEE Access 7, 18940–18950 (2019)CrossRefGoogle Scholar
  13. 13.
    Barachan, A., Bonnet, S., Congedp, M.: Multiclass brain-computer interface classification by riemannian geometry. IEEE Trans. Biomed. Eng. 59(4), 920–928 (2011)CrossRefGoogle Scholar
  14. 14.
    Gaur, P., Pachori, R.B., Wang, H., et al.: A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry. Expert Syst. Appl. 95(1), 201–211 (2018)CrossRefGoogle Scholar
  15. 15.
    Hersche, M., Rellstab, T., Schiavone, P.D., et al.: Fast and accurate multiclass inference for mi-bcis using large multiscale temporal and spectral features. In: 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, pp. 1690–1694. IEEE (2018)Google Scholar
  16. 16.
    Brunner, C., Leeb, R., Muller-Putz, R., Schlogl, A., Pfurtscheller, G.: BCI competition 2008 - Graz data set A. Accessed 10 July 2019
  17. 17.
    Razi, S., Mollaei, M.R.K., Ghasemi, J.: A novel method for classification of BCI mul-ti-class motor imagery task based on Dempster-Shafer theory. Inf. Sci. 484, 14–26 (2019)CrossRefGoogle Scholar
  18. 18.
    Wang, H.X.: Multiclass filters by a weighted pairwise criterion for EEG single-trial classification. IEEE Trans. Biomed. Eng. 58(5), 1412–1420 (2011)CrossRefGoogle Scholar
  19. 19.
    Kam, T.E., Suk, H.I., Lee, S.W.: Non-homogeneous spatial filter optimization for EEG-based brain-computer interfaces. In: International Winter Workshop on Brain-Computer Interface, Gangwo, South Korea, pp. 26–28. IEEE (2013)Google Scholar
  20. 20.
    Nicolas-Alonso, L.F., Corralejo, R., Gomez-Pilar, J., et al.: Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain computer interfaces. Neurocomputing 159, 186–196 (2015)CrossRefGoogle Scholar
  21. 21.
    He, L.H., Hu, D., Wan, M., et al.: Common Bayesian network for classification of EEG-Based multiclass motor imagery BCI. IEEE Trans. Syst. Man Cybern. Syst. 46(6), 843–854 (2016)CrossRefGoogle Scholar
  22. 22.
    Xie, X.F., Yu, Z.L., Gu, Z.H., et al.: Bilinear regularized locality preserving learning on riemannian graph for motor imagery BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 26(3), 698–708 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Information EngineeringCapital Normal UniversityBeijingChina
  2. 2.Laboratory of Neural Computing and Intelligent Perception (NCIP)New YorkUSA
  3. 3.International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical InterdisciplinaryBeijingChina
  4. 4.Beijing Advanced Innovation Center for Imaging TechnologyBeijingChina
  5. 5.School of MedicineStanford UniversityStanfordUSA

Personalised recommendations