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

Research output: Chapter in Book/ReportConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditorsSergio Velastin, Marcelo Mendoza
Number of pages8
ISBN (Print)9783319751924
StatePublished - 2018
Event22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile
Duration: Nov 7 2017Nov 10 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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