Classification of Gait Motor Imagery While Standing Based on Electroencephalographic Bandpower

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


Brain-computer interfaces (BCIs) translate brain signals into commands for a device. BCIs are a complementary option in therapy during gait rehabilitation. This paper presents a strategy based on electroencephalographic (EEG) bandpower for detecting gait motor imagery (MI) while being standing. In particular, \(\mu \) (8–13 Hz) and 20–35 Hz bands were used. Preliminary results show that two out of three users could achieve an accuracy above 70% of correct classifications. The proposed strategy could be used in a MI-based BCI to enhance brain activity associated to the gait process.


Sensorimotor \(\mu \) rhythm Motor imagery BCI EEG 



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”. Also, the Mexican Council of Science and Technology (CONACyT) provided I.N. Angulo-Sherman her scholarship.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CINVESTAV, Monterrey’s UnitApodacaMexico
  2. 2.Brain-Machine Interface Systems Lab, Systems Engineering and Automation DepartmentMiguel Hernández University of ElcheElche (Alicante)Spain

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