Appling of Neural Networks to Classification of Brain-Computer Interface Data

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 613)


The paper presents application of neural networks to the construction of a brain-computer interface (BCI) based on the Motor Imagery paradigm. The BCI was constructed for ten electroencephalographic (EEG) signals collected and analysed in real time.The filtered signals were divided into three groups corresponding to the information displayed to users on the screen during the experiments. ANOVA analysis and automatic construction of a neural network (NN) classification were also performed. Results of the ANOVA analysis were confirmed by the neural networks efficiency analysis. The efficiency of NN classification of the left and right hemisphere activities reached almost 70 %.


Neural networks Brain-computer interface EEG data ANOVA 


  1. 1.
    An, X., Kuang, D., Guo, X., Zhao, Y., He, L.: A deep learning method for classification of EEG data based on motor imagery. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 203–210. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Barbati, G., Porcaro, C., Zappasodi, F., Rossini, P., Tecchio, F.: Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clin. Neurophysiol. 115, 1220–1232 (2004)CrossRefGoogle Scholar
  3. 3.
    Blinowska, K., Kaminski, M.: Multivariate signal analysis by parametric models. In: Schelter, B., Winterhalder, M., Timmer, J. (eds.) Handbook of Time SeriesAnalysis. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim (2006)Google Scholar
  4. 4.
    Bromfield, E., Cavazos, J., Sirven, J.: Basic mechanisms underlying seizures and epilepsy. In: An Introduction to Epilepsy (2006)Google Scholar
  5. 5.
    Broniec-Wojcik, A.: Ph.D. dissertation. AGH, Krakow (2013)Google Scholar
  6. 6.
    Cho, B., Lee, J., Ku, J., Jang, D., Kim, J., Kim, I., Kim, S.: Attention enhancement system using virtual reality and EEG biofeedback. In: Virtual Reality, Proceedings, IEEE, pp. 156–163 (2002)Google Scholar
  7. 7.
    Croft, R., Barry, R.: Eog correction: a new perspective. Electroencephalogr. Clin. Neurophysiol. 107, 387–394 (1998)CrossRefGoogle Scholar
  8. 8.
    Croft, R., Barry, R.: Removal of ocular artifact from the EEG: a review. Neuro. Physiol. Clin. 30, 5–19 (2000)Google Scholar
  9. 9.
    Diab, M., Ismail, G., Al-Jawha, M., Hsaiky, A., Moslem, B., Sabbah, M., Taha, M.: Biofeedback for epilepsy treatment. In: Mechatronics and its Applications (ISMA), pp. 1–4. IEEE (2012)Google Scholar
  10. 10.
    Geethanjali, P., Mohan, Y., Sen, J.: Time domain feature extraction and classification of eeg data for brain computer interface. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE (2012)Google Scholar
  11. 11.
    Joyce, C., Gorodnitsky, I., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiol. 41, 313–325 (2004)CrossRefGoogle Scholar
  12. 12.
    Jung, T., Makeig, S., Humphries, C., Lee, T.W., Mckeown, M.J., Iragui, V., Sejnowski, T.J.: Removingelectroencephalographic artifacts by blind source separation. Psychophysiol. 37, 163–178 (2000)CrossRefGoogle Scholar
  13. 13.
    al-Ketbi, O., Conrad, M.: Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: Why can GMDH do better? Int. J. Comput. Appl. 74(4), 37–44 (2013)Google Scholar
  14. 14.
    Kornhuber, H.H., Deecke, L.: Changes in human brain potentials before and after voluntary movement studied by recording on magnetic tape and reverse analysis (1964)Google Scholar
  15. 15.
    Koronacki, J., Cwik, J.: Statisticcal learning systems (in Polish: Statystyczne systemy uczace sie), Exit (2008)Google Scholar
  16. 16.
    Lee, S., Abibullaev, B., Kang, W., Shin, Y., An, J.: Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps. In: Control Automation and Systems (ICCAS), pp. 2439–2442 (2010)Google Scholar
  17. 17.
    Mingyu, L., Jue, W., Nan, Y., Qin, Y.: Development of EEG biofeedback system based on virtual reality environment. In: Engineering in Medicine and Biology Society, pp. 5362–5364 (2005)Google Scholar
  18. 18.
    Neuper, C., Miller, G., Kebler, A., Birbaumer, N., Pfurtscheller, G.: Clinical application of an eeg-based brain-computer interface: a case study in a patient with severe motor impairment. Clin. Neurophysiol. 114(3), 399–409 (2003)CrossRefGoogle Scholar
  19. 19.
    Nowak-Brzezińska, A., Jach, T.: The incompleteness factor method as a support of inference in decision support systems. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 201–210. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  20. 20.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)CrossRefGoogle Scholar
  21. 21.
    Pfurtscheller, G., da Silva, L.: Functional Meaning of Event-Related Desynchronization (ERD) and Synchronization (ERS), pp. 51–65 (1999)Google Scholar
  22. 22.
    Shim, B., Lee, S.W., Shin., J.H.: Implementation of a 3-dimensional game fordeveloping balanced brainwave. In: Software Engineering Research, Management & Applications, SERA 2007 (2007)Google Scholar
  23. 23.
    Suresh, K., Heng, J.: Quantitative eeg parameters for monitoring and biofeedback during rehabilitation after stroke. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2009)Google Scholar
  24. 24.
    Van Vliet, M., Robben, A., Chumerin, N., Manyakov, N., Combaz, A., Van Hulle, M.: Designing a brain-computer interface controlled video-game using consumer grade EEG hardware. In: Biosignals and Biorobotics Conference (BRC 2012), pp. 1–6. ISSNIP (2012)Google Scholar
  25. 25.
    Vidal, J.: Toward direct brain-computer communication. Ann. Rev. Biophys. Bioeng. 2(1), 157–180 (1973)CrossRefGoogle Scholar
  26. 26.
    Wolpaw, J., Birbaumer, N., Heetderks, W., McFarland, D., Peckham, P., Schalk, G., Vaughan, T.: Brain-computer interface technology: a review of the firstinternational meeting. IEEE Trans. Rehabil. Eng. 8(2), 164–173 (2000)CrossRefGoogle Scholar
  27. 27.
    Żbikowski, K.: Time series forecasting with volume weighted support vector machines. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 250–258. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  28. 28.
    Zielosko, B.: Optimization of inhibitory decision rules relative to coverage - comparative studys. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 521, pp. 267–276. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Lublin University of TechnologyLublinPoland

Personalised recommendations