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Using Morphological-Linear Neural Network for Upper Limb Movement Intention Recognition from EEG Signals

  • Gerardo HernándezEmail author
  • Luis G. Hernández
  • Erik Zamora
  • Humberto Sossa
  • Javier M. Antelis
  • Omar Mendoza-Montoya
  • Luis E. Falcón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms for the intention recognition to perform different movements from electroencephalographic (EEG) signals. Three classification models were implemented and assessed to decode EEG motor imagery signals: (i) Morphological-Linear Neural Network (MLNN) (ii) Support Vector Machine (SVM) and (iii) Multilayer perceptron (MLP). Real EEG signals recorded during robot-assisted rehabilitation therapy were used to evaluate the performance of the proposed algorithms in the classification of three classes (relax, movement intention A Int A and movement intention B Int B) using multi-CSP based features extracted from EEG signals. The results of a ten-fold cross validation show similar results in terms of classification accuracy for the SVM and MLNN models. However, the number of parameters used in each model varies considerably (the MLNN model use less parameters than the SVM). This study indicates potential application of MLNNs for decoding movement intentions and its use to develop more natural and intuitive robot assisted neurorehabilitation therapies.

Keywords

Brain-computer interfaces Morphological-linear neural network Movement planing Machine learning Electroencephalogram 

Notes

Acknowledgments

This work was partially supported by the National Council of Science and Technology of Mexico (CONACYT) through grant PN2015-873 and scholarship 291197. H. Sossa and E. Zamora would like to acknowledge the support provided by CONACYT grant number 65 (Frontiers of Science) and SIP-IPN (grant numbers 20180180, 20190166 and 20190007). G. Hernández acknowledges CONACYT for the scholarship granted towards pursuing his PhD studies.

References

  1. 1.
    Vega, R., et al.: Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artif. Intell. Res. 6(1), 37 (2017)MathSciNetGoogle Scholar
  2. 2.
    Figueroa-Garcia, I., et al.: Platform for the study of virtual task-oriented motion and its evaluation by EEG and EMG biopotentials. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1174–1177, August 2014Google Scholar
  3. 3.
    Dornhege, G., Blankertz, B., Curio, G., Muller, K.: 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.
    Shiman, F., et al.: Classification of different reaching movements from the same limb using EEG. J. Neural Eng. 14(4), 046018 (2017)CrossRefGoogle Scholar
  5. 5.
    Yong, X., Menon, C.: EEG classification of different imaginary movements within the same limb. PLOS One 10(4), 1–24 (2015)CrossRefGoogle Scholar
  6. 6.
    Van Der Malsburg, C.: Frank Rosenblatt: principles of neurodynamics: perceptrons and the theory of brain mechanisms. In: Palm, G., Aertsen, A. (eds.) Brain Theory, pp. 245–248. Springer, Heidelberg (1986).  https://doi.org/10.1007/978-3-642-70911-1_20CrossRefGoogle Scholar
  7. 7.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Neurocomputing: Foundations of Research, pp. 696–699. MIT Press, Cambridge (1988)Google Scholar
  8. 8.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  10. 10.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144–152. ACM, New York (1992)Google Scholar
  11. 11.
    Hernández, G., Zamora, E., Sossa, H.: Morphological-linear neural network. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6, July 2018Google Scholar
  12. 12.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Ofner, P., Schwarz, A., Pereira, J., Müller-Putz, G.R.: Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS One 12(8), e0182578 (2017). PONE-D-17-04785[PII]CrossRefGoogle Scholar
  14. 14.
    Pereira, J., Sburlea, A.I., Müller-Putz, G.R.: EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets. Sci. Rep. 8(1), 13394 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gerardo Hernández
    • 2
    Email author
  • Luis G. Hernández
    • 1
  • Erik Zamora
    • 2
  • Humberto Sossa
    • 1
    • 2
  • Javier M. Antelis
    • 1
  • Omar Mendoza-Montoya
    • 1
  • Luis E. Falcón
    • 1
  1. 1.Tecnológico de Monterrey en GuadalajaraZapopanMexico
  2. 2.Instituto Politécnico Nacional - CICCiudad de MéxicoMexico

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