Towards a Roadmap for Machine Learning and EEG-Based Brain Computer Interface

  • Taline NobregaEmail author
  • Severino Netto
  • Rommel Araujo
  • Allan Martins
  • Edgard Morya
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1068)


The technological revolution of the last decades allowed the development of algorithms to perform tasks computationally demanding. Electroencephalography (EEG) signal processing typically requires complex protocols for feature extraction. Machine learning emerged as a potential tool to demystify these complications. Brain-computer interface (BCI) researchers have already implemented machine learning techniques to solve complicated paradigms. These studies achieved significant results and suggested that machine learning could accelerate complex data analysis. This study aims to develop quantitative bibliometric research, i.e., a roadmap, to evaluate the development of studies involving machine learning and BCI applications, especially motor imagery protocols. The results showed that machine learning provides innovative solutions for motor imagery studies. Although, there were few publications related to machine learning and BCI, it is clear that the scientific applications are growing fast, and are developing higher-performance EEG-based approaches.


Machine learning BCI Motor imagery Roadmap 


  1. 1.
    Abdulkader, S.N., Atia, A., Mostafa, M.S.: Brain computer interfacing: applications and challenges. Egypt. Inform. J. 16(2), 213–230 (2015)CrossRefGoogle Scholar
  2. 2.
    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, 270–278 (2018)CrossRefGoogle Scholar
  3. 3.
    Acharya, U.R., et al.: A novel depression diagnosis index using nonlinear features in EEG signals. Eur. Neurol. 74(1–2), 79–83 (2015)CrossRefGoogle Scholar
  4. 4.
    An, X., Kuang, D., Guo, X., Zhao, Y., He, L.: A deep learning method for classification of EEG data based on motor imagery. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 203–210. Springer, Cham (2014). Scholar
  5. 5.
    Ang, K.K., Guan, C.: EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25(4), 392–401 (2017)CrossRefGoogle Scholar
  6. 6.
    Aurlien, H., et al.: EEG background activity described by a large computerized database. Clin. Neurophysiol. 115(3), 665–673 (2004)CrossRefGoogle Scholar
  7. 7.
    Aznan, N.K.N., Bonner, S., Connolly, J., Al Moubayed, N., Breckon, T.: On the classification of SSVEP-based dry-EEG signals via convolutional neural networks. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3726–3731. IEEE (2018)Google Scholar
  8. 8.
    Buch, E., et al.: Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39(3), 910–917 (2008)CrossRefGoogle Scholar
  9. 9.
    Caria, A., et al.: Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 48(4), 578–582 (2011)CrossRefGoogle Scholar
  10. 10.
    Duncan, C.C., et al.: Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin. Neurophysiol. 120(11), 1883–1908 (2009)CrossRefGoogle Scholar
  11. 11.
    Haas, L.F.: Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography. J. Neurol. Neurosurg. Psychiatry 74(1), 9 (2003)CrossRefGoogle Scholar
  12. 12.
    Hamadicharef, B.: Brain-computer interface (BCI) literature - a bibliometric study, pp. 626–629, June 2010.
  13. 13.
    Hsu, W.Y., et al.: Unsupervised fuzzy C-means clustering for motor imagery EEG recognition. Int. J. Innov. Comput. Inf. Control 7, 4965–4976 (2011)Google Scholar
  14. 14.
    Isa, N.M., Amir, A., Ilyas, M., Razalli, M.: Motor imagery classification in brain computer interface (BCI) based on EEG signal by using machine learning technique. Bull. Electr. Eng. Inform. 8(1), 269–275 (2019)Google Scholar
  15. 15.
    Jeannerod, M.: Mental imagery in the motor context. Neuropsychologia 33(11), 1419–1432 (1995)CrossRefGoogle Scholar
  16. 16.
    Kostoff, R.N., Schaller, R.R.: Science and technology roadmaps. IEEE Trans. Eng. Manag. 48(2), 132–143 (2001). Scholar
  17. 17.
    Lai, T., et al.: A brain computer interface with online feedback based on magnetoencephalography. In: ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning, pp. 465–472 (2005)Google Scholar
  18. 18.
    Lee, H.K., Choi, Y.S.: A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image. In: 2018 International Conference on Information Networking (ICOIN), pp. 906–909. IEEE (2018)Google Scholar
  19. 19.
    Li, T., Zhang, J., Xue, T., Wang, B.: Development of a novel motor imagery control technique and application in a gaming environment. Comput. Intell. Neurosci. 2017, 1–16 (2017) Google Scholar
  20. 20.
    Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., Li, Y.: Deep learning based on batch normalization for P300 signal detection. Neurocomputing 275, 288–297 (2018)CrossRefGoogle Scholar
  21. 21.
    Lotte, F., et al.: Combining BCI with virtual reality: towards new applications and improved BCI. In: Allison, B., Dunne, S., Leeb, R., Millán, J.D.R., Nijholt, A. (eds.) Towards Practical Brain-Computer Interfaces. Biological and Medical Physics, Biomedical Engineering, pp. 197–220. Springer, Heidelberg (2012). Scholar
  22. 22.
    Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans. Neural Syst. Rehabil. Eng. 25(6), 566–576 (2016)CrossRefGoogle Scholar
  23. 23.
    McFarland, D.J., Krusienski, D.J., Wolpaw, J.R.: Brain-computer interface signal processing at the Wadsworth center: mu and sensorimotor beta rhythms. Prog. Brain Res. 159, 411–419 (2006)CrossRefGoogle Scholar
  24. 24.
    Murphy, S.M.: Imagery interventions in sport. Med. Sci. Sports Exerc. 26(4), 486–494 (1994) CrossRefGoogle Scholar
  25. 25.
    Oganesyan, V.V., Agapov, S.N., Bulanov, V.A., Biryukova, E.V.: Comparison of results obtained using brain-computer interface classifiers in a motor imagery recognition task. Neurosci. Behav. Physiol. 48(9), 1164–1168 (2018)CrossRefGoogle Scholar
  26. 26.
    Oh, S.L., et al.: A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl., 1–7 (2018) Google Scholar
  27. 27.
    Page, S.J., Levine, P., Leonard, A.C.: Effects of mental practice on affected limb use and function in chronic stroke. Arch. Phys. Med. Rehabil. 86(3), 399–402 (2005)CrossRefGoogle Scholar
  28. 28.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001). Scholar
  29. 29.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Da Silva, F.L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  30. 30.
    Phaal, R., Farrukh, C.J., Probert, D.R.: Technology roadmapping - a planning framework for evolution and revolution. Technol. Forecast. Soc. Chang. 71, 5–26 (2004)CrossRefGoogle Scholar
  31. 31.
    Prakaksita, N., Kuo, C.Y., Kuo, C.H.: Development of a motor imagery based brain-computer interface for humanoid robot control applications. In: 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 1607–1613. IEEE (2016)Google Scholar
  32. 32.
    Rabha, J., Nagarjuna, K.Y., Samanta, D., Mitra, P., Sarma, M.: Motor imagery EEG signal processing and classification using machine learning approach. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS), pp. 61–66, October 2017.
  33. 33.
    Ramos-Murguialday, A., et al.: Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74(1), 100–108 (2013)CrossRefGoogle Scholar
  34. 34.
    Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  35. 35.
    Shim, M., Hwang, H.J., Kim, D.W., Lee, S.H., Im, C.H.: Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr. Res. 176(2–3), 314–319 (2016)CrossRefGoogle Scholar
  36. 36.
    Sirigu, A., et al.: Congruent unilateral impairments for real and imagined hand movements. Neuroreport 6(7), 997–1001 (1995)CrossRefGoogle Scholar
  37. 37.
    de Souza, L.B., M.D.F.C., Borschiver, S.: Formas de onda e o programa rds-defesa: Proposta e resultados do roadmap tecnológico do lte para aplicações militares. In: XXXVI Simposio Brasileiro de Telecomunicações e Processamento de Sinais - SBrt2018 (2018) Google Scholar
  38. 38.
    Trambaiolli, L.R., Lorena, A.C., Fraga, F.J., Kanda, P.A., Anghinah, R., Nitrini, R.: Improving alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42(3), 160–165 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Taline Nobrega
    • 1
    Email author
  • Severino Netto
    • 2
  • Rommel Araujo
    • 2
  • Allan Martins
    • 1
  • Edgard Morya
    • 2
  1. 1.Post-Graduation Program in Electrical and Computer Engineering (PPGEEC)Federal University of Rio Grande do NorteNatalBrazil
  2. 2.Neuroengineering Program, Edmond and Lily Safra International Neuroscience Institute, Santos Dumont InstituteMacaibaBrazil

Personalised recommendations