Using EEG Signals to Detect Different Surfaces While Walking

  • Raúl Chapero
  • Eduardo IáñezEmail author
  • Marisol Rodríguez-Ugarte
  • Mario Ortiz
  • José M. Azorín
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


Brain-Computer Interfaces are one of the most interesting ways to work in rehabilitation and assistance programs to people who have problems in their lower limb to march. This paper presents evidence by means of statistical analysis sets that there are specific frequencies ranges on EEG signals while walking on four different surfaces: hard floor, soft floor, ramp and stairs, finding proportional differences in predictions between each pair of tasks for every user through the employ of Matlab classifiers. In that way, our results are statistical sets of successful percentages in classification of signals between two tasks. We worked with five different volunteers and we found an average of 76.5% of success in predictions between soft floor and stairs surfaces. Lower results, around 60%, were obtained when differentiating between hard floor/stairs and ramp/stairs. We can notice that magnitude of these percentages fits with a common sense about real physical differences between four kinds of surfaces. This study means a starting point to go deeper in signal morphology analyzing the specific mathematical characteristics of EEG signals while walking on those surfaces and other ones.


Surface detection EEG Signals BCI Systems Walking 



This research has been carried out in the framework of the project Associate - Decoding and stimulation of motor and sensory brain activity to support long term potentiation through Hebbian and paired associative stimulation during rehabilitation of gait (DPI2014-58431-C4-2-R), funded by the Spanish Ministry of Economy and Competitiveness and by the European Union through the European Regional Development Fund (ERDF) A way to build Europe.

The acquisition wireless system of EEG signals with 32 channels from Brain Products has been partially financed by funds from the European Union (P.O. FEDER 2007/2013), with the management of Generalitat Valenciana (Spain).


  1. 1.
    World Health Organization: Global health and ageing, Geneva (Switzerland), World Health Organization (2011)Google Scholar
  2. 2.
    Mann, W.C., Hurren, D., Tomita, M.: Comparison of assistive device use and needs of home-based older persons with different impairments. Am. J. Occup. Ther. 47(11), 980–987 (1993)CrossRefGoogle Scholar
  3. 3.
    Akdogan, E., Adli, M.A.: The design and control of a therapeutic exercise robot for lower limb rehabilitation: physiotherabot. Mechatronics 21(3), 509–522 (2011)CrossRefGoogle Scholar
  4. 4.
    Espregueira-Mendes, J., Pereira, R.B., Monteiro, A.: Lower limb rehabilitation. In: Margheritini, F., Rossi, R. (eds.) Orthopedic Sports Medicine, pp. 485–495. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Kong, K., Jeon, D.: Design and control of an exoskeleton for the elderly and patients. IEEE/ASME Trans. Mechatron. 11(4), 428–432 (2006)CrossRefGoogle Scholar
  6. 6.
    Miskelly, F.G.: Assistive technology in elderly care. Age Ageing 30(6), 455–458 (2001)CrossRefGoogle Scholar
  7. 7.
    Pohl, M., Werner, C., Holzgraefe, M., Kroczek, G., Wingendorf, I., Holig, G., Koch, R., Hesse, S.: A single-blind, randomized multicentre trial, degas. Clin. Rehabil. 21(1), 17–27 (2007)CrossRefGoogle Scholar
  8. 8.
    Hortal, E., Mrquez-Snchez, E., Costa., Piuela-Martn, E., Salazar, R., del-Ama, A.J., Gil-Agudo, A., Azorn, J.M.: Starting and finishing gait detection using a BMI for spinal cord injury rehabilitation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), Innovative Session on Wearable Robotics for Motion Assistance and Rehabilitation, pp. 6184-6189, Hamburg, Germany (2015)Google Scholar
  9. 9.
    Hanawaka, T.: Organizing motor imageries. Neurosci. Res. 104, 56–63 (2016)CrossRefGoogle Scholar
  10. 10.
    Costa, Á., Iáñez, E., Úbeda, A., Del-Ama, A.J., et al.: Decoding the attentional demands of gait through EEG gamma band features. PLoS ONE 11(4), e0154136 (2016)CrossRefGoogle Scholar
  11. 11.
    Salazar-Varas, R., Costa, Á., Iáñez, E., Úbeda, A., Hortal, E., Azorín, J.M.: Analyzing EEG signals to detect unexpected obstacles during walking. J. NeuroEng. Rehabil. 12(101), 1–15 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raúl Chapero
    • 1
  • Eduardo Iáñez
    • 1
    Email author
  • Marisol Rodríguez-Ugarte
    • 1
  • Mario Ortiz
    • 1
  • José M. Azorín
    • 1
  1. 1.Brain-Machine Interface Systems Lab, Systems Engineering and Automation DepartmentMiguel Hernández University of ElcheElche (Alicante)Spain

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