Advertisement

Discrimination of Shoulder Flexion/Extension Motor Imagery Through EEG Spatial Features to Command an Upper Limb Robotic Exoskeleton

  • Ramón Amado Reinoso-LeblanchEmail author
  • Yunier Prieur-Coloma
  • Leondry Mayeta-Revilla
  • Roberto Sagaró-Zamora
  • Denis Delisle-Rodriguez
  • Teodiano Bastos
  • Alberto López-Delis
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

This work presents a comparison between two methods for spatial feature extraction applied on a system to recognize shoulder flexion/extension motor imagery (SMI) tasks to convey on-line control commands towards a 4 degrees-of-freedom (DoF) upper-limb robotic exoskeleton. Riemannian geometry and Common Spatial Pattern (CSP) are applied on the filtered EEG for spatial feature extraction, which later are used by the Linear Discriminant Analysis (LDA) classifier for motor imagery (MI) recognition. Three bipolar EEG channels were used on six healthy subjects to acquire our database, composed of two classes: rest state and shoulder flexion/extension MI. Our system achieved a mean accuracy (ACC) of 75.12% applying Riemannian, with the highest performance for Subject S01 (ACC = 89.68%, Kappa = 79.37%, true positive rate (TPR) = 87.50%, and FPR < 8.13%). In contrast, for CSP, a mean ACC of 66.29% was achieved. These findings suggest that unsupervised methods for feature extraction, such as Riemannian geometry, can be suitable for shoulder flexion/extension MI to command an upper-limb robotic exoskeleton.

Keywords

Riemannian geometry Common Spatial Pattern Brain-computer interface Motor imagery Upper limb Robotic exoskeleton Shoulder movement intention 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The authors would like to thank the Medical Biophysics Center (Centro de Biofísica Médica) of Cuba, UFES/Brazil, and the Belgian Development Cooperation, through VLIR-UO (Flemish Interuniversity Council-University Cooperation for Development), in the context of the Institutional University Cooperation program with the University of Oriente for supporting this research.

References

  1. 1.
    Lotte, F., et al.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)CrossRefGoogle Scholar
  2. 2.
    Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. OUP, New York (2012)CrossRefGoogle Scholar
  3. 3.
    Pfurtscheller, G., Da Silva, F.L.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)CrossRefGoogle Scholar
  4. 4.
    Ang, K.K., et al.: A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface. Clin. EEG Neurosci. 42(4), 253–258 (2011)CrossRefGoogle Scholar
  5. 5.
    Soekadar, S.R., et al.: ERD-based online brain–machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance. IEEE Trans. Neural Syst. Rehabil. Eng. 19(5), 542–549 (2011)CrossRefGoogle Scholar
  6. 6.
    Ono, T., et al.: Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. 7, 19 (2014)CrossRefGoogle Scholar
  7. 7.
    Rimbert, S., et al.: Can a subjective questionnaire be used as brain-computer interface performance predictor? Front. Hum. Neurosci. 12, 529 (2018)CrossRefGoogle Scholar
  8. 8.
    Neuper, C., et al.: Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin. Neurophysiol. 120(2), 239–247 (2009)CrossRefGoogle Scholar
  9. 9.
    Tang, Z.-C., et al.: Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI. Front. Inf. Technol. Electron. Eng. 20, 1087–1098 (2019)CrossRefGoogle Scholar
  10. 10.
    Ang, K.K., Guan, C.: Brain–computer interface for neurorehabilitation of upper limb after stroke. Proc. IEEE 103(6), 944–953 (2015)CrossRefGoogle Scholar
  11. 11.
    Torres, M., et al.: Robotic system for upper limb rehabilitation. In: Braidot, A., Hadad, A. (eds.) VI Latin American Congress on Biomedical Engineering CLAIB 2014, vol. 49, pp. 948–951. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-13117-7_240CrossRefGoogle Scholar
  12. 12.
    Vidaurre, C., et al.: Time domain parameters as a feature for EEG-based brain–computer interfaces. Neural Netw. 22(9), 1313–1319 (2009)CrossRefGoogle Scholar
  13. 13.
    Sannelli, C., et al.: A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity. PLoS ONE 14(1), e0207351 (2019)CrossRefGoogle Scholar
  14. 14.
    Blankertz, B., et al.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig. Process. Mag. 25(1), 41–56 (2008)CrossRefGoogle Scholar
  15. 15.
    Yger, F., Berar, M., Lotte, F.: Riemannian approaches in brain-computer interfaces: a review. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1753–1762 (2017)CrossRefGoogle Scholar
  16. 16.
    Barachant, A., et al.: Multiclass brain–computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 59(4), 920–928 (2012)CrossRefGoogle Scholar
  17. 17.
    Barachant, A., et al.: Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172–178 (2013)CrossRefGoogle Scholar
  18. 18.
    Kalunga, E., Chevallier, S., Barthélemy, Q.: Data augmentation in Riemannian space for brain-computer interfaces. In: STAMLINS (2015)Google Scholar
  19. 19.
    Petersen, P.: Riemannian Geometry, vol. 171. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-26654-1CrossRefzbMATHGoogle Scholar
  20. 20.
    Vidaurre, C., et al.: A fully on-line adaptive BCI. IEEE Trans. Biomed. Eng. 53(6), 1214–1219 (2006)CrossRefGoogle Scholar
  21. 21.
    Osuagwu, B.C., et al.: Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study. J. Neural Eng. 13(6), 065002 (2016)CrossRefGoogle Scholar
  22. 22.
    Scherer, R., et al.: Toward self-paced brain–computer communication: navigation through virtual worlds. IEEE Trans. Biomed. Eng. 55(2), 675–682 (2008)CrossRefGoogle Scholar
  23. 23.
    Lou, B., et al.: Bipolar electrode selection for a motor imagery based brain–computer interface. J. Neural Eng. 5(3), 342 (2008)CrossRefGoogle Scholar
  24. 24.
    Cho, H., et al.: A step-by-step tutorial for a motor imagery–based BCI. In: Brain–Computer Interfaces Handbook, pp. 445–460. CRC Press, Boca Raton (2018)CrossRefGoogle Scholar
  25. 25.
    Tang, Z., Li, C., Sun, S.: Single-trial EEG classification of motor imagery using deep convolutional neural networks. Opt.-Int. J. Light. Electron Opt. 130, 11–18 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ramón Amado Reinoso-Leblanch
    • 1
    Email author
  • Yunier Prieur-Coloma
    • 1
  • Leondry Mayeta-Revilla
    • 2
  • Roberto Sagaró-Zamora
    • 3
  • Denis Delisle-Rodriguez
    • 4
  • Teodiano Bastos
    • 4
  • Alberto López-Delis
    • 1
  1. 1.Centre of Medical BiophysicsUniversity of OrienteSantiago de CubaCuba
  2. 2.Department of Biomedical EngineeringUniversity of OrienteSantiago de CubaCuba
  3. 3.Faculty of Mechanical EngineeringUniversity of OrienteSantiago de CubaCuba
  4. 4.Postgraduate Program in Electrical EngineeringFederal University of Espirito SantoVitoriaBrazil

Personalised recommendations

Cite paper

Buy options