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Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm

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  • S. M. Fernandez-Fraga
  • M. A. Aceves-Fernandez
  • J. C. Pedraza-Ortega
  • S. Tovar-Arriaga

Abstract

This work presents the use of swarm intelligence algorithms as a reliable method for the optimization of electroencephalogram signals for the improvement of the performance of the brain interfaces based on stable states visual events. The preprocessing of brain signals for the extraction of characteristics and the detection of events is of paramount importance for the improvement of brain interfaces. The proposed ant colony optimization algorithm presents an improvement in obtaining the key features of the signals and the detection of events based on visual stimuli. As a reference model, we used the Independent Component Analysis method, which has been used in recent research for the removal of nonrelevant and detection of relevant data from the brain’s electrical signals and also allows the collection of information in response to a stimulus and separates the signals that were generated independently in certain zones of the brain.

Suggested Citation

  • S. M. Fernandez-Fraga & M. A. Aceves-Fernandez & J. C. Pedraza-Ortega & S. Tovar-Arriaga, 2018. "Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-19, June.
    • Handle: RePEc:hin:jnddns:2143873
    DOI: 10.1155/2018/2143873
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    Cited by:

    1. Wu, Tao & Gao, Xiangyun & An, Feng & Xu, Xin & Kurths, Jürgen, 2024. "Forecasting the dynamics of correlations in complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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