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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)

Abstract

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.

Keywords

Surface detection EEG Signals BCI Systems Walking 

Notes

Acknowledgments

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).

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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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