Advertisement

Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models

  • Carlos Daniel Virgilio Gonzalez
  • Juan Humberto Sossa Azuela
  • Elsa Rubio Espino
  • Victor H. Ponce Ponce
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements; in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks; Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks; Subject 1 - \(93.9\% \pm 3.9\%\) and Subject 2 - \(68.7\% \pm 7\%\).

Keywords

EEG signals Motor Imagery Common Spatial Pattern RMS One vs Rest Pair-Wise Dendrite Morphological Neural Network Multilayer Perceptron 

Notes

Acknowledgements

We would like to express our sincere appreciation to the Instituto Politécnico Nacional and the Secretaria de Investigación y Posgrado for the economic support provided to carry out this research. This project was supported economically by SIP-IPN (numbers 20180730, 20180943 and 20180846) and CONACYT (65 Frontiers of Science).

References

  1. 1.
    Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., Bagheri, N.: Multiple classifier system for EEG signal classification with application to brain-computer interfaces. Neural Comput. Appl. 23(5), 1319–1327 (2013).  https://doi.org/10.1007/s00521-012-1074-3CrossRefGoogle Scholar
  2. 2.
    Antelis, J.M., Gudiño-Mendoza, B., Falcón, L.E., Sanchez-Ante, G., Sossa, H.: Dendrite morphological neural networks for motor task recognition from electroencephalographic signals. Biomed. Signal Process. Control. 44, 12–24 (2018).  https://doi.org/10.1016/j.bspc.2018.03.010CrossRefGoogle Scholar
  3. 3.
    Asensio Cubero, J., Gan, J.Q., Palaniappan, R.: Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing. Biomed. Signal Process. Control. 8(6), 772–778 (2013).  https://doi.org/10.1016/j.bspc.2013.07.004CrossRefGoogle Scholar
  4. 4.
    Bayliss, J.D.: Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 113–116 (2003).  https://doi.org/10.1109/TNSRE.2003.814438MathSciNetCrossRefGoogle Scholar
  5. 5.
    Belhadj, S.A., Benmoussat, N., Krachai, M.D.: CSP features extraction and FLDA classification of EEG-based motor imagery for brain-computer interaction. In: 2015 4th International Conference on Electrical Engineering, ICEE 2015, pp. 3–8 (2016).  https://doi.org/10.1109/INTEE.2015.7416697
  6. 6.
    Chin, Z.Y., Ang, K.K., Wang, C., Guan, C., Zhang, H.: Multi-class filter bank common spatial pattern for four-class motor imagery BCI. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, vol. 138632, pp. 571–574 (2009).  https://doi.org/10.1109/IEMBS.2009.5332383
  7. 7.
    Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain- computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000).  https://doi.org/10.1109/86.847808CrossRefGoogle Scholar
  8. 8.
    Han, R.X., Wei, Q.G.: Feature extraction by combining wavelet packet transform and common spatial pattern in brain-computer interfaces. Appl. Mech. Mater. 239, 974–979 (2013).  https://doi.org/10.4028/www.scientific.net/AMM.239-240.974CrossRefGoogle Scholar
  9. 9.
    Higashi, H., Tanaka, T.: Simultaneous design of FIR filter banks and spatial patterns for EEG signal classification. IEEE Trans. Biomed. Eng. 60(4), 1100–1110 (2013).  https://doi.org/10.1109/TBME.2012.2215960CrossRefGoogle Scholar
  10. 10.
    Hosni, S.M., Gadallah, M.E., Bahgat, S.F., AbdelWahab, M.S.: Classification of EEG signals using different feature extraction techniques for mental-task BCI. In: 2007 International Conference on Computer Engineering Systems, pp. 220–226 (2007).  https://doi.org/10.1109/ICCES.2007.4447052, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4447052
  11. 11.
    Iturrate, I., Antelis, J.M., Andrea, K., Minguez, J.: A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans. Robot. 25(3), 614–627 (2009)CrossRefGoogle Scholar
  12. 12.
    Katona, J., Kovari, A.: EEG-based computer control interface for brain-machine interaction. Int. J. Online Eng. 11(6), 43–48 (2015).  https://doi.org/10.3991/ijoe.v11i6.5119CrossRefGoogle Scholar
  13. 13.
    Li, M., Li, W., Zhao, J., Meng, Q., Zeng, M., Chen, G.: A P300 model for cerebot – a mind-controlled humanoid robot. In: Kim, J.-H., Matson, E.T., Myung, H., Xu, P., Karray, F. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 495–502. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05582-4_43CrossRefGoogle Scholar
  14. 14.
    Li, Y., et al.: An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans. Biomed. Eng. 57(10 PART 1), 2495–2505 (2010).  https://doi.org/10.1109/TBME.2010.2055564CrossRefGoogle Scholar
  15. 15.
    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(5), 667–677 (2016).  https://doi.org/10.1155/2016/4941235MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Mulder, T.: Motor imagery and action observation: cognitive tools for rehabilitation. J. Neural Transm. 114(10), 1265–1278 (2007).  https://doi.org/10.1007/s00702-007-0763-zCrossRefGoogle Scholar
  17. 17.
    Muller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H., Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial fiters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110(5), 787–798 (1999).  https://doi.org/10.1016/S1388-2457(98)00038-8. http://www.sciencedirect.com/science/article/pii/S1388245798000388CrossRefGoogle Scholar
  18. 18.
    Ritter, G.X., Sussner, P.: An introduction to morphological neural networks. In: Proceedings - International Conference on Pattern Recognition, vol. 4, pp. 709–717 (1996).  https://doi.org/10.1109/ICPR.1996.547657
  19. 19.
    Sossa, H., Guevara, E.: Efficient training for dendrite morphological neural networks. Neurocomputing 131, 132–142 (2014).  https://doi.org/10.1016/j.neucom.2013.10.031CrossRefGoogle Scholar
  20. 20.
    Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78(3), 252–259 (1991).  https://doi.org/10.1016/0013-4694(91)90040-B, http://www.sciencedirect.com/science/article/pii/001346949190040BCrossRefGoogle Scholar
  21. 21.
    Zeidan, F., Martucci, K.T.: Brain mechanisms supporting modulation of pain by mindfulness meditation. J. Neurosci.: Off. J. Soc. Neurosci. 31(14), 5540–5548 (2011).  https://doi.org/10.1523/JNEUROSCI.5791-10.2011.BrainCrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85–91 (2015).  https://doi.org/10.1016/j.jneumeth.2015.08.004CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Carlos Daniel Virgilio Gonzalez
    • 1
  • Juan Humberto Sossa Azuela
    • 1
    • 2
  • Elsa Rubio Espino
    • 1
  • Victor H. Ponce Ponce
    • 1
  1. 1.Centro de Investigación en Computación – Instituto Politécnico NacionalMexico CityMexico
  2. 2.Tecnológico de Monterrey, Escuela de Ingeniería y CienciasZapopanMexico

Personalised recommendations