Brain Computer Interface Enhancement by Independent Component Analysis

  • Pavel Bobrov
  • Alexander A. Frolov
  • Dušan Húsek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)

Abstract

Brain-Computer Interface is aimed as a direct communication pathway between human or animal brain and an external device. A reliable, accurate and fast identification of a being’s intention based on EEG signal scanning is crucial part of the system. To improve the classification accuracy we propose to use Independent Component Analysis for \(\mu \)-rhythm identification in data corresponding to motor imagery task performance during Brain-Computer Interface training and operation. We show that independent components corresponding to the \(\mu \)-rhythm allow for higher classification accuracy comparing to raw EEG recordings usage.

Keywords

Classification Accuracy Independent Component Motor Imagery Independent Component Analysis Mental Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This paper has been partly elaborated in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070, supported by Operational Programme ‘Research and Development for Innovations’ funded by Structural Funds of the European Union and state budget of the Czech Republic and partly supported by the projects AV0Z10300504, GACR P202/10/0262, 205/09/1079.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pavel Bobrov
    • 1
  • Alexander A. Frolov
    • 2
  • Dušan Húsek
    • 3
  1. 1.Institute for Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences and VSB Technical University of OstravaOstravaCzech Republic
  2. 2.Mathematical neurobilogy of learningInstitute for Higher Nervous Activity, Neurophysiology of Russian Academy of SciencesMoscowRussian Federation
  3. 3.Nonlinear ModellingInstitute of Computer Science, Academy of Sciences of the Czech RepublicPragueCzech Republic

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