Motor Imagery Task Classification in EEG Signals with Spiking Neural Network

  • Carlos D. Virgilio G
  • Humberto SossaEmail author
  • Javier M. Antelis
  • Luis E. Falcón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)


We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network.


EEG signals Motor Imagery Power Spectral Density Wavelet Decomposition Neural networks Multi layer perceptron Spiking Neural Network 



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 and 20190007) and the National Council of Science and Technology of Mexico (CONACyT) (65 Frontiers of Science, numbers 268958 and PN2015-873).


  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). 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. Sig. Process. Control 44, 12–24 (2018). 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. Sig. Process. Control 8(6), 772–778 (2013). Scholar
  4. 4.
    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).
  5. 5.
    Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). Scholar
  6. 6.
    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). Scholar
  7. 7.
    Izhikevich, E.M.: Dynamical Systems in Neuroscience Computational Neuroscience (2007).
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization, vol. 4, pp. 1942–1948 (1995).
  9. 9.
    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). Scholar
  10. 10.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997). Scholar
  11. 11.
    McFarland, D.J., Miner, L.A., Vaughan, T.M., Wolpaw, J.R.: Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 12(3), 177–186 (2000)CrossRefGoogle Scholar
  12. 12.
    Mulder, T.: Motor imagery and action observation: cognitive tools for rehabilitation. J. Neural Transm. 114(10), 1265–1278 (2007). Scholar
  13. 13.
    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).
  14. 14.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Lopes da Silva, F.H.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage (2006).
  15. 15.
    Ponulak, F.: Allen - 2011 - Introduction to spiking neural networks Information processing, learning and applications, January 2011Google Scholar
  16. 16.
    Virgilio Gonzalez, C.D., Sossa Azuela, J.H., Rubio Espino, E., Ponce Ponce, V.H.: Classification of motor imagery EEG signals with CSP filtering through neural networks models. In: Batyrshin, I., Martínez-Villaseñor, M.L., Ponce Espinosa, H.E. (eds.) MICAI 2018. LNCS (LNAI), vol. 11288, pp. 123–135. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlos D. Virgilio G
    • 1
  • Humberto Sossa
    • 1
    • 2
    Email author
  • Javier M. Antelis
    • 2
  • Luis E. Falcón
    • 2
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico
  2. 2.Tecnológico de Monterrey, Escuela de Ingeniería y CienciasZapopanMexico

Personalised recommendations