An EEG Brain-Computer Interface to Classify Motor Imagery Signals

  • Maria Karoline Andrade
  • Maí­ra Araújo de Santana
  • Giselle Moreno
  • Igor Oliveira
  • Jhonnatan Santos
  • Marcelo Cairrão Araújo Rodrigues
  • Wellington Pinheiro dos SantosEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


Considering the increase in life expectancy, people started to invest in technologies capable of improving the quality of life. One of these technologies is the Brain-Machine Interface. Combined with EEG signals, this technique may allow individuals with some motor disabilities to perform activities of daily living. Motor Imagery came up as an important tool to support this population. So they may send commands to external devices by using their brain voluntary activity. In this chapter, the performance of an Imagery EEG-based BCI engine was accessed by applying Wavelet transform to the signals and extracting metrics used to describe digital signals. We used signals from the motor imagery of the right hand, left hand and foot movements. Different intelligent classifiers were tested. We achieved results greater than 99% of accuracy and Kappa above 0.99. The method is promising and can be used for future evaluations with several individuals to verify reproducibility.



We are grateful to the Brazilian research-funding agencies CAPES, CNPq and Facepe, for the partial support for this research.


  1. 1.
    Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100(1), 270–278 (2018)CrossRefGoogle Scholar
  2. 2.
    Ai, Q., Chen, A., Chen, K., Liu, Q., Zhou, T., Xin, S., Ji, Z.: Feature extraction of four-class motor imagery EEG signals based on functional brain network. J. Neural Eng. (2019). Scholar
  3. 3.
    Akay, M.: Wavelet applications in medicine. IEEE Spectr. (1997)Google Scholar
  4. 4.
    Al-Timemy, A.H., Bugmann, G., Escudero, J., Outram, N.: A preliminary investigation of the effect of force variation for myoelectric control of hand prosthesis. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5758–5761. IEEE (2013)Google Scholar
  5. 5.
    Al-Timemy, A.H., Khushaba, R.N., Bugmann, G., Escudero, J.: Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 24(6), 650–661 (2016)CrossRefGoogle Scholar
  6. 6.
    Alazrai, R., Alwanni, H., Daoud, M.I.: EEG-based BCI system for decoding finger movements within the same hand. Neurosci. Lett. 698, 113–120 (2019)CrossRefGoogle Scholar
  7. 7.
    Azevedo, W.W., Lima, S.M., Fernandes, I.M., Rocha, A.D., Cordeiro, F.R., da Silva-Filho, A.G., dos Santos, W.P.: Fuzzy morphological extreme learning machines to detect and classify masses in mammograms. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2015)Google Scholar
  8. 8.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32 (2007)CrossRefGoogle Scholar
  9. 9.
    Bauer, S.M., Elsaesser, L.J., Arthanat, S.: Assistive technology device classification based upon the World Health Organization’s, International Classification of Functioning, Disability and Health (ICF). Disabil. Rehabil. Assist. Technol. 6(3), 243–259 (2011)CrossRefGoogle Scholar
  10. 10.
    Bergmeister, K.D., Hader, M., Lewis, S., Russold, M.F., Schiestl, M., Manzano-Szalai, K., Roche, A.D., Salminger, S., Dietl, H., Aszmann, O.C.: Prosthesis control with an implantable multichannel wireless electromyography system for high-level amputees: a large-animal study. Plast. Reconstr. Surg. 137(1), 153–162 (2016)CrossRefGoogle Scholar
  11. 11.
    Birbaumer, N., Cohen, L.G.: Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579(3), 621–636 (2007)CrossRefGoogle Scholar
  12. 12.
    Borg, J., Larsson, S., Östergren, P.O.: The right to assistive technology: for whom, for what, and by whom? Disabil. Soc. 26(2), 151–167 (2011)CrossRefGoogle Scholar
  13. 13.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Advances in Neural Information Processing Systems, pp. 409–415 (2001)Google Scholar
  15. 15.
    Cheng, J., Greiner, R.: Learning Bayesian belief network classifiers: algorithms and system. In: Stroulia, E., Matwin, S. (eds.) Advances in Artificial Intelligence, pp. 141–151. Springer, Berlin, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Chui, C.K.: An Introduction to Wavelets, 1st edn. Academic Press, New York (1992)Google Scholar
  17. 17.
    Controzzi, M., Clemente, F., Barone, D., Ghionzoli, A., Cipriani, C.: The SSSA-MyHand: a dexterous lightweight myoelectric hand prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 25(5), 459–468 (2017)CrossRefGoogle Scholar
  18. 18.
    Cordeiro, F.R., dos Santos, W.P., da Silva-Filho, A.G.: Segmentation of mammography by applying GrowCut for mass detection. Stud. Health Technol. Inform. 192, 87–91 (2013)Google Scholar
  19. 19.
    Cordeiro, F.R., Santos, W.P., Silva-Filho, A.G.: A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Syst. Appl. 65, 116–126 (2016)CrossRefGoogle Scholar
  20. 20.
    Cruz, T., Cruz, T., Santos, W.: Detection and classification of lesions in mammographies using neural networks and morphological wavelets. IEEE Lat. Am. Trans. 16(3), 926–932 (2018)CrossRefGoogle Scholar
  21. 21.
    Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7(11), 1032–1043 (2008)CrossRefGoogle Scholar
  22. 22.
    Dyson, M., Barnes, J., Nazarpour, K.: Myoelectric control with abstract decoders. J. Neural Eng. (2018)Google Scholar
  23. 23.
    Eaton, J.W., Bateman, D., Hauberg, S.: GNU Octave manual version 3: a high-level interactive language for numerical computations. Netw. Theory (2008)Google Scholar
  24. 24.
    Eide, A.H., Øderud, T.: Assistive technology in low-income countries. In: Disability & International Development, pp. 149–160. Springer (2009)Google Scholar
  25. 25.
    Feng, G., Huang, G.B., Lin, Q., Gay, R.K.L., et al.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Netw. 20(8), 1352–1357 (2009)CrossRefGoogle Scholar
  26. 26.
    Fong, S., Biuk-Aghai, R.P., Millham, R.C.: Swarm search methods in Weka for data mining. In: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018, pp. 122–127. ACM, New York, NY, USA (2018).
  27. 27.
    de Freitas, R.C., Alves, R., da Silva-Filho, A.G., de Souza, R.E., Bezerra, B.L.D., dos Santos, W.P.: Electromyography-controlled car: a proof of concept based on surface electromyography, extreme learning machines and low-cost open hardware. Comput. Electr. Eng. 73, 167–179 (2019)CrossRefGoogle Scholar
  28. 28.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)zbMATHCrossRefGoogle Scholar
  29. 29.
    Ghazaei, G., Alameer, A., Degenaar, P., Morgan, G., Nazarpour, K.: Deep learning-based artificial vision for grasp classification in myoelectric hands. J. Neural Eng. 14(3), 036,025 (2017)CrossRefGoogle Scholar
  30. 30.
    (g.tec), G.T.: Common Spatial Patterns 3-class BCI, vol. v2.16.00. Guger Technologies, Schiedlberg, Austria (2016)Google Scholar
  31. 31.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  32. 32.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New York (1999)Google Scholar
  33. 33.
    Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994, pp. 357–361. IEEE (1994)Google Scholar
  34. 34.
    Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 42(2), 513–529 (2012)Google Scholar
  35. 35.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, 2004, vol. 2, pp. 985–990. IEEE (2004)Google Scholar
  36. 36.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRefGoogle Scholar
  37. 37.
    Ison, M., Artemiadis, P.: The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control. J. Neural Eng. 11(5), 051,001 (2014)CrossRefGoogle Scholar
  38. 38.
    Jung, Y., Hu, J.: A K-fold averaging cross-validation procedure. J. Nonparametric Stat. 27, 167–179 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Kasun, L.L.C., Zhou, H., Huang, G.B., Vong, C.M.: Representational learning with extreme learning machine for big data. IEEE Intell. Syst. 28(6), 31–34 (2013)Google Scholar
  40. 40.
    Khalaf, A., Sejdic, E., Akcakaya, M.: A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound. J. Neurosci. Methods 313(1), 44–53 (2019)CrossRefGoogle Scholar
  41. 41.
    Khushaba, R.N., Al-Timemy, A., Kodagoda, S., Nazarpour, K.: Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst. Appl. 61, 154–161 (2016)CrossRefGoogle Scholar
  42. 42.
    Khushaba, R.N., Krasoulis, A., Al-Jumaily, A., Nazarpour, K.: Spatio-temporal inertial measurements feature extraction improves hand movement pattern recognition without electromyography. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2108–2111. IEEE (2018)Google Scholar
  43. 43.
    Lécuyer, A., Lotte, F., Reilly, R.B., Leeb, R., Hirose, M., Slater, M.: Brain-computer interfaces, virtual reality, and videogames. Computer 41(10), (2008)CrossRefGoogle Scholar
  44. 44.
    Li, J., Sun, S.: Energy feature extraction of EEG signals and a case study. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2366–2370 (2008)Google Scholar
  45. 45.
    Liu, J.: Feature dimensionality reduction for myoelectric pattern recognition: a comparison study of feature selection and feature projection methods. Med. Eng. Phys. 36(12), 1716–1720 (2014)CrossRefGoogle Scholar
  46. 46.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007)CrossRefGoogle Scholar
  47. 47.
    Martin, S., Kelly, G., Kernohan, W.G., McCreight, B., Nugent, C.: Smart home technologies for health and social care support. Cochrane Database Syst. Rev. 4(2) (2008)Google Scholar
  48. 48.
    Martínez-Martínez, J.M., Escandell-Montero, P., Soria-Olivas, E., MartíN-Guerrero, J.D., Magdalena-Benedito, R., GóMez-Sanchis, J.: Regularized extreme learning machine for regression problems. Neurocomputing 74(17), 3716–3721 (2011)CrossRefGoogle Scholar
  49. 49.
    Massopust, P.R.: Fractal Functions, Fractal Surfaces and Wavelets, 1st edn. Academic Press, New York (1994)zbMATHCrossRefGoogle Scholar
  50. 50.
    Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: optimally pruned extreme learning machine. IEEE Trans. Neural Netw. 21(1), 158–162 (2010)CrossRefGoogle Scholar
  51. 51.
    Middendorf, M., McMillan, G., Calhoun, G., Jones, K.S.: Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans. Rehabil. Eng. 8(2), 211–214 (2000)CrossRefGoogle Scholar
  52. 52.
    Millán, J.d.R., Rupp, R., Müller-Putz, G., Murray-Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kubler, A., Leeb, R., et al.: Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front. Neurosci. 4, 161 (2010)Google Scholar
  53. 53.
    Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces: a review. Sensors 12(2), 1211–1279 (2012)CrossRefGoogle Scholar
  54. 54.
    Organization, W.H.: International classification of functioning, disability, and health: children & youth version: ICF-CY. World Health Organization (2007)Google Scholar
  55. 55.
    Pahwa, P., Papreja, M., Miglani, R.: Performance analysis of classification algorithms. Int. J. Comput. Sci. Mobile Comput. 3(4), 50–58 (2014)Google Scholar
  56. 56.
    Pfurtscheller, G., Neuper, C.: Motor imagery activates primary sensorimotor area in humans. Neurosci. Lett. 239, 65–68 (1997)CrossRefGoogle Scholar
  57. 57.
    Qureshi, M.N.I., Cho, D., Lee, B.: EEG classification for motor imagery BCI using phase-only features extracted by independent component analysis. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2097–2100 (2017).
  58. 58.
    Roman-Gonzalez, A.: EEG signal processing for BCI l. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human-Computer Systems Interaction: Backgrounds and Applications 2: Part 1, pp. 571–591. Springer, Berlin, Heidelberg (2012)Google Scholar
  59. 59.
    Saha, S., Mamun, K.A., Ahmed, K., Mostafa, R., Naik, G.R., Khandoker, A., Darvishi, S., Baumert, M.: Progress in brain computer interfaces: challenges and trends (2019). arXiv:1901.03442
  60. 60.
    Samuel, O.W., Geng, Y., Li, X., Li, G.: Towards efficient decoding of multiple classes of motor imagery limb movements based on eeg spectral and time domain descriptors. J. Med. Syst. 41(12), 194 (2017)CrossRefGoogle Scholar
  61. 61.
    dos Santos, M.M., da Silva Filho, A.G., dos Santos, W.P.: Deep convolutional extreme learning machines: filters combination and error model validation. Neurocomputing 329, 359–369 (2019)CrossRefGoogle Scholar
  62. 62.
    dos Santos, W.P., de Assis, F.M., de Souza, R.E., Mendes, P.B., de Souza Monteiro, H.S., Alves, H.D.: A dialectical method to classify Alzheimer’s magnetic resonance images. In: Evolutionary Computation. InTech (2009)Google Scholar
  63. 63.
    dos Santos, W.P., de Assis, F.M., de Souza, R.E., dos Santos Filho, P.B.: Evaluation of Alzheimer’s disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, EMBS 2008, pp. 5506–5509. IEEE (2008)Google Scholar
  64. 64.
    dos Santos, W.P., de Assis, F.M., de Souza, R.E., Santos-Filho, P.B., de Lima Neto, F.B.: Dialectical multispectral classification of diffusion-weighted magnetic resonance images as an alternative to apparent diffusion coefficients maps to perform anatomical analysis. Comput. Med. Imaging Graph. 33(6), 442–460 (2009)CrossRefGoogle Scholar
  65. 65.
    dos Santos, W.P., de Souza, R.E., dos Santos Filho, P.B.: Evaluation of Alzheimer’s disease by analysis of MR images using multilayer perceptrons and Kohonen SOM classifiers as an alternative to the ADC maps. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, EMBS 2007, pp. 2118–2121. IEEE (2007)Google Scholar
  66. 66.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)CrossRefGoogle Scholar
  67. 67.
    Scheme, E., Englehart, K.: Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J. Rehabil. Res. Dev. 48(6) (2011)CrossRefGoogle Scholar
  68. 68.
    Scherer, M.J.: Assessing the benefits of using assistive technologies and other supports for thinking, remembering and learning. Disabil. Rehabil. 27(13), 731–739 (2005)CrossRefGoogle Scholar
  69. 69.
    Scherer, M.J., Glueckauf, R.: Assessing the benefits of assistive technologies for activities and participation. Rehabil. Psychol. 50(2), 132 (2005)CrossRefGoogle Scholar
  70. 70.
    Schwartz, A.B., Cui, X.T., Weber, D.J., Moran, D.W.: Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52, 205–220 (2006)CrossRefGoogle Scholar
  71. 71.
    Spüler, M.: A high-speed brain-computer interface (BCI) using dry EEG electrodes. Plos One 12(2), 1–12 (2017)CrossRefGoogle Scholar
  72. 72.
    Trakoolwilaiwan, T., Behboodi, B., Lee, J., Kim, K., Choi, J.W.: Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution. Neurophotonics 5(1), 5–15 (2017)CrossRefGoogle Scholar
  73. 73.
    de Vasconcelos, J.H., dos Santos, W.P., de Cássia Fernandes de Lima, R.: Analysis of methods of classification of breast thermographic images to determine their viability in the early breast cancer detection. IEEE Lat. Am. Trans. 16(6), 1631 (2018)Google Scholar
  74. 74.
    Wolpaw, J., Wolpaw, E.W.: Brain-computer interfaces: principles and practice. OUP, USA (2012)CrossRefGoogle Scholar
  75. 75.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)CrossRefGoogle Scholar
  76. 76.
    Yuan, Q., Zhou, W., Li, S., Cai, D.: Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 96(1), 29–38 (2011)CrossRefGoogle Scholar
  77. 77.
    Zander, T.O., Kothe, C.: Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng. 8(2), 025,005 (2011)CrossRefGoogle Scholar
  78. 78.
    Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recognit. 38(10), 1759–1763 (2005)zbMATHCrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Maria Karoline Andrade
    • 1
  • Maí­ra Araújo de Santana
    • 1
  • Giselle Moreno
    • 1
  • Igor Oliveira
    • 1
  • Jhonnatan Santos
    • 1
  • Marcelo Cairrão Araújo Rodrigues
    • 1
  • Wellington Pinheiro dos Santos
    • 1
    Email author
  1. 1.Universidade Federal de PernambucoRecifeBrazil

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