Nature-Inspired Algorithms for Selecting EEG Sources for Motor Imagery Based BCI

  • Sebastián BasterrechEmail author
  • Pavel Bobrov
  • Alexander Frolov
  • Dušan Húsek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9120)


In this article we examine the performance of two well-known metaheuristic techniques (Genetic Algorithm and Simulating Annealing) for selecting the input features of a classifier in a BCI system. An important problem of the EEG-based BCI system consists in designing the EEG pattern classifier. The selection of the EEG channels used for building that learning predictor has impact in the classifier performance. We present results of both metaheuristic techniques on real data set when the classifier is a Bayesian predictor. We statistically compare that performances with a random selection of the EEG channels. According our empirical results our approach significantly increases the accuracy of the learning predictor.


Brain computer interface EEG pattern selection Bayesian classifier Genetic algorithms Simulating annealing 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sebastián Basterrech
    • 1
    Email author
  • Pavel Bobrov
    • 1
    • 2
  • Alexander Frolov
    • 2
  • Dušan Húsek
    • 3
  1. 1.VŠB-Technical University of OstravaOstravaCzech Republic
  2. 2.Institute of Higher Nervous Activity and NeurophysiologgyRASMoscowRussia
  3. 3.Institute of Computer ScienceAcademy of Sciences of the Czech Republic PraguePrahaCzech Republic

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