Self-organizing Maps for Motor Tasks Recognition from Electrical Brain Signals

  • Alvaro D. Orjuela-Cañón
  • Osvaldo Renteria-Meza
  • Luis G. Hernández
  • Andrés F. Ruíz-Olaya
  • Alexander Cerquera
  • Javier M. Antelis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Recently, there has been a relevant progress and interest for brain–computer interface (BCI) technology as a potential channel of communication and control for the motor disabled, including post-stroke and spinal cord injury patients. Different mental tasks, including motor imagery, generate changes in the electro-physiological signals of the brain, which could be registered in a non-invasive way using electroencephalography (EEG). The success of the mental motor imagery classification depends on the choice of features used to characterize the raw EEG signals, and of the adequate classifier. As a novel alternative to recognize motor imagery tasks for EEG-based BCI, this work proposes the use of self-organized maps (SOM) for the classification stage. To do so, it was carried out an experiment aiming to predict three-class motor tasks (rest versus left motor imagery versus right motor imagery) utilizing spectral power-based features of recorded EEG signals. Three different pattern recognition algorithms were applied, supervised SOM, SOM+k-means and k-means, to classify the data offline. Best results were obtained with the SOM trained in a supervised way, where the mean of the performance was 77% with a maximum of 85% for all classes. Results indicate potential application for the development of BCIs systems.


Electroencephalogram Brain-computer interfaces Motor imagery Classification Self-organized maps 



The authors thank to Universidad Antonio Nariño under project 2016207 and publication PI/UAN-2017-611GIBIO for the support in this work. J.M. Antelis thanks the financial support of CONACYT through grants 268958 and PN2015-873.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad Antonio NariñoBogota D.C.Colombia
  2. 2.Tecnologico de Monterrey, Campus GuadalajaraZapopanMexico

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