Advertisement

Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion

  • Piotr SzczukoEmail author
  • Michał Lech
  • Andrzej Czyżewski
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 138)

Abstract

The classification of EEG signals provides an important element of brain-computer interface (BCI) applications, underlying an efficient interaction between a human and a computer application. The BCI applications can be especially useful for people with disabilities. Numerous experiments aim at recognition of motion intent of left or right hand being useful for locked-in-state or paralyzed subjects in controlling computer applications. The chapter presents an experimental study of several methods for real motion and motion intent classification (rest/upper/lower limbs motion, and rest/left/right hand motion). First, our approach to EEG recordings segmentation and feature extraction is presented. Then, 5 classifiers (Naïve Bayes, Decision Trees, Random Forest, Nearest-Neighbors NNge, Rough Set classifier) are trained and tested using examples from an open database. Feature subsets are selected for consecutive classification experiments, reducing the number of required EEG electrodes. Methods comparison and obtained results are presented, and a study of features feeding the classifiers is provided. Differences among participating subjects and accuracies for real and imaginary motion are discussed. It is shown that though classification accuracy varies from person to person, it could exceed 80% for some classifiers.

Keywords

Motion intent classification EEG signal analysis Rough sets 

Notes

Acknowledgements

The research is funded by the National Science Centre of Poland on the basis of the decision DEC-2014/15/B/ST7/04724.

References

  1. 1.
    Alotaiby, T., El-Samie, F.E., Alshebeili S.A.: A review of channel selection algorithms for eeg signal processing. EURASIP. J. Adv. Signal Process, 66 (2015)Google Scholar
  2. 2.
    BCI2000. Bci2000 instrumentation system project. http://www.bci2000.org, Accessed on 2017-03-01
  3. 3.
    Bek, J., Poliakoff, E., Marshall, H., Trueman, S., Gowen, E.: Enhancing voluntary imitation through attention and motor imagery. Exp. Brain Res. 234, 1819–1828 (2016)CrossRefGoogle Scholar
  4. 4.
    Bhattacharyya, S., Konar, A., Tibarewala, D.N.: Motor imagery, p300 and error-related eeg-based robot arm movement control for rehabilitation purpose. Med. Biol. Eng. Comput. 52, 2014 (1007)Google Scholar
  5. 5.
    Chen, S., Lai, Y.A.: Sgnal-processing-based technique for p300 evoked potential detection with the applications into automated character recognition. EURASIP. J. Adv. Signal Process. 152 (2014)Google Scholar
  6. 6.
    Choi, K.: Electroencephalography (eeg)-based neurofeedback training for brain-computer interface (bci). Exp. Brain Res. 231, 351–365 (2013)CrossRefGoogle Scholar
  7. 7.
    Corralejo, R., Nicolas-Alonso, L.F., Alvarez, D., Hornero, R.: A p300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people. Med. Biol. Eng. Comput. 52, 861–872 (2014)CrossRefGoogle Scholar
  8. 8.
    Czyżewski, A., Kostek, B., Kurowski, A., Szczuko, P., Lech, M., Odya, P., Kwiatkowska, A.: Assessment of hearing in coma patients employing auditory brainstem response, electroencephalography and eye-gaze-tracking. In: Proceedings of the 173rd Meeting of the Acoustical Society of America (2017)Google Scholar
  9. 9.
    Dickhaus, T., Sannelli, C., Muller, K.R., Curio, G., Blankertz, B.: Predicting bci performance to study bci illiteracy. BMC Neurosci. 10 (2009)Google Scholar
  10. 10.
    Diez, P.F., Mut, V.A., Avila Perona, E.M.: Asynchronous bci control using high-frequency. SSVEP. J. NeuroEngineering. Rehabil. 8(39) (2011)Google Scholar
  11. 11.
    Doud, A.J., Lucas, J.P., Pisansky, M.T., He, B.: Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS ONE. 6(10) (2011)Google Scholar
  12. 12.
    Faller, J., Scherer, R., Friedrich, E., Costa, U., Opisso, E., Medina, J., Muller-Putz, G.R.: Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment. Front. Neurosci., 8 (2014)Google Scholar
  13. 13.
    Gardener, M., Beginning, R.: The statistical programming language, (2012). https://cran.r-project.org/manuals.html, Accessed on 2017-03-01
  14. 14.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101, 215–220 (2000)Google Scholar
  15. 15.
    He, B., Gao, S., Yuan, H., Wolpaw, JR.: Brain-computer interfaces, In: He, B. (ed.) Neural Engineering, pp. 87–151 (2012).  https://doi.org/10.1007/978-1-4614-5227-0_2
  16. 16.
    Iscan, Z.: Detection of p300 wave from eeg data for brain-computer interface applications. Pattern Recognit. Image Anal. 21(481) (2011)Google Scholar
  17. 17.
    Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. In: Proceedings of the International Conference on Rough Sets and Knowledge Technology (RSKT), number 6954 in Lecture Notes in Artificial Intelligence, pp. 45–50 (2011)Google Scholar
  18. 18.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)Google Scholar
  19. 19.
    Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37, 163–178 (2000)Google Scholar
  20. 20.
    Kasahara, T., Terasaki, K., Ogawa, Y.: The correlation between motor impairments and event-related desynchronization during motor imagery in als patients. BMC Neurosci. 13(66) (2012)Google Scholar
  21. 21.
    Kayikcioglu, T., Aydemir, O.: A polynomial fitting and k-nn based approach for improving classification of motor imagery bci data. Pattern Recognit. Lett. 31(11), 1207–1215 (2010)CrossRefGoogle Scholar
  22. 22.
    Krepki, R., Blankertz, B., Curio, G., Muller, K.R.: The berlin brain-computer interface (bbci) - towards a new communication channel for online control in gaming applications. Multimed. Tools Appl. 33, 73–90 (2007)CrossRefGoogle Scholar
  23. 23.
    Kumar, S.U., Inbarani, H.: Pso-based feature selection and neighborhood rough set-based classification for bci multiclass motor imagery task. Neural Comput. Appl. 33, 1–20 (2016)Google Scholar
  24. 24.
    LaFleur, K., Cassady, K., Doud, A.J., Shades, K., Rogin, E., He, B.: Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface. J. Neural. Eng. 10 (2013)Google Scholar
  25. 25.
    Leeb, R., Pfurtscheller, G.: Walking through a virtual city by thought. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, (2004)Google Scholar
  26. 26.
    Leeb, R., Scherer, R., Lee, F., Bischof, H., Pfurtscheller, G.: Navigation in virtual environments through motor imagery. In: Proceedings of the 9th Computer Vision Winter Workshop, pp. 99–108, (2004)Google Scholar
  27. 27.
    Marple, S.L.: Computing the discrete-time analytic signal via fft. IEEE Trans. Signal Proc. 47, 2600–2603 (1999)CrossRefzbMATHGoogle Scholar
  28. 28.
    Martin, B.: Instance-based learning: nearest neighbour with generalization. Technical report, University of Waikato, Department of Computer Science, Hamilton, New Zealand (1995)Google Scholar
  29. 29.
    Nakayashiki, K., Saeki, M., Takata, Y.: Modulation of event-related desynchronization during kinematic and kinetic hand movements. J. NeuroEng. Rehabil. 11(90) (2014)Google Scholar
  30. 30.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefzbMATHGoogle Scholar
  31. 31.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. of IEEE 89, 1123–1134 (2001)CrossRefGoogle Scholar
  32. 32.
    Pfurtscheller, G., Brunner, C., Schlogl, A., Lopes, F.H.: Mu rhythm (de)synchronization and eeg single-trial classification of different motor imagery tasks. NeuroImage 31, 153–159 (2006)CrossRefGoogle Scholar
  33. 33.
    Postelnicu, C., Talaba, D.: P300-based brain-neuronal computer interaction for spelling applications. IEEE Trans. Biomed. Eng. 60, 534–543 (2013)CrossRefGoogle Scholar
  34. 34.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  35. 35.
    Riza, S.L., Janusz, A., Slezak, D., Cornelis, C., Herrera, F., Benitez, J.M., Bergmeir, C., Stawicki, S.; Roughsets: data analysis using rough set and fuzzy rough set theories, (2015). https://github.com/janusza/RoughSets, Accessed on 2017-03-01
  36. 36.
    Roy, S.: Nearest neighbor with generalization. Christchurch, New Zealand (2002)Google Scholar
  37. 37.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: Bci 2000: A general-purpose brain-computer interface (bci) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)CrossRefGoogle Scholar
  38. 38.
    Schwarz, A., Scherer, R., Steyrl, D., Faller, J., Muller-Putz, G.: Co-adaptive sensory motor rhythms brain-computer interface based on common spatial patterns and random forest. In: Proceedings of the 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), (2015)Google Scholar
  39. 39.
    Shan, H., Xu, H., Zhu, S., He, B.: A novel channel selection method for optimal classification in different motor imagery bci paradigms. BioMed. Eng. OnLine, 14 (2015)Google Scholar
  40. 40.
    Silva, J., Torres-Solis, J., Chau, T.: A novel asynchronous access method with binary interfaces. J. NeuroEng. Rehabil. 5(24) (2008)Google Scholar
  41. 41.
    Siuly, S., Li, Y.: Improving the separability of motor imagery eeg signals using a cross correlation-based least square support vector machine for brain computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526–538 (2012)CrossRefGoogle Scholar
  42. 42.
    Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery eeg signals employing naive bayes based learning process. J. Measurement 86, 148–158 (2016)CrossRefGoogle Scholar
  43. 43.
    Suh, D., Sang Cho, H., Goo, J., Park, K.S., Hahn, M.: Virtual navigation system for the disabled by motor imagery. In: Advances in Computer, Information, and Systems Sciences, and Engineering, pp. 143–148 (2006).  https://doi.org/10.1007/1-4020-5261-8_24
  44. 44.
    Szczuko, P., Lech, M., Czyżewski, A.: Comparison of methods for real and imaginary motion classification from eeg signals. In: Proceedings of ISMIS conference, (2017)Google Scholar
  45. 45.
    Szczuko, P.: Real and imagery motion classification based on rough set analysis of eeg signals for multimedia applications. Multimed. Tools Appl. (2017).  https://doi.org/10.1007/s11042-017-4458-7
  46. 46.
    Szczuko, P.: Rough set-based classification of eeg signals related to real and imagery motion. In: Proceedings Signal Processing Algorithms, Architectures, Arrangements, and Applications, (2016)Google Scholar
  47. 47.
    Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: A user-friendly application for meg/eeg analysis. Comput. Intell. Neurosci. vol. 2011, Article ID 879716 (2011).  https://doi.org/10.1155/2011/879716
  48. 48.
    Tesche, C.D., Uusitalo, M.A., Ilmoniemi, R.J., Huotilainen, M., Kajola, M., Salonen, O.: Signal-space projections of meg data characterize both distributed and well-localized neuronal sources. Electroencephalogr. Clin. Neurophysiol. 95, 189–200 (1995)CrossRefGoogle Scholar
  49. 49.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)Google Scholar
  50. 50.
    Ungureanu, M., Bigan, C., Strungaru, R., Lazarescu, V.: Independent component analysis applied in biomedical signal processing. Measurement Sci. Rev. 4, 1–8 (2004)Google Scholar
  51. 51.
    Uusitalo, M.A., Ilmoniemi, R.J.: Signal-space projection method for separating meg or eeg into components. Med. Biol. Eng. Comput. 35, 135–140 (1997)CrossRefGoogle Scholar
  52. 52.
    Velasco-Alvarez, F., Ron-Angevin, R., Lopez-Gordo, M.A.: Bci-based navigation in virtual and real environments. IWANN. LNCS 7903, 404–412 (2013)Google Scholar
  53. 53.
    Vidaurre, C., Blankertz, B.: Towards a cure for bci illiteracy. Brain Topogr. 23, 194–198 (2010)CrossRefGoogle Scholar
  54. 54.
    Witten, I.H., Frank, E., Hall, M.A.: Data mining: Practical machine learning tools and techniques. In: Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann (2011). www.cs.waikato.ac.nz/ml/weka/, Accessed Mar 1st 2017
  55. 55.
    Wu, C.C., Hamm, J.P., Lim, V.K., Kirk, I.J.: Mu rhythm suppression demonstrates action representation in pianists during passive listening of piano melodies. Exp. Brain Res. 234, 2133–2139 (2016)CrossRefGoogle Scholar
  56. 56.
    Xia, B., Li, X., Xie, H.: Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn. Comput. 5(243) (2013)Google Scholar
  57. 57.
    Yang, J., Singh, H., Hines, E., Schlaghecken, F., Iliescu, D.: Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif. Intell. Med. 55, 117–126 (2012)CrossRefGoogle Scholar
  58. 58.
    Yuan, H., He, B.: Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans. Biomed. Eng. 61, 1425–1435 (2014)CrossRefGoogle Scholar
  59. 59.
    Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., Yao, D.: Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS ONE. 8(9) (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Piotr Szczuko
    • 1
    Email author
  • Michał Lech
    • 1
  • Andrzej Czyżewski
    • 1
  1. 1.Faculty of Electronics, Telecommunications and InformaticsGdańsk University of TechnologyGdańskPoland

Personalised recommendations