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Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification

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Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

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Abstract

Feature Selection (FS) is a contemporary challenge for the scientific community since new methods are being discovered and new forms of algorithmic design are required. In this sense, classic bio-inspired swarm intelligence algorithms can be explored to get a new suitable feature selection application where simple agents interact locally with each other while searching a global solution. Therefore, this work proposes an innovative utilization of the ant colony optimization algorithm for FS (ACO-FS) with the objective of reducing the high number of features present in Electroencephalogram (EEG) signals. Specifically, a base ant colony optimization algorithm and two variants have been implemented and evaluated in terms of execution time, energy consumption, and classification rates to point out the strength and weakness of each variant. The preliminary results demonstrate that the proposed method provides a classification rate close to 86% when the whole dataset is reduced from 3,600 features to only 22.

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Acknowledgments

This research has been funded by the Spanish Ministry of Science, Innovation, and Universities under grant PGC2018-098813-B-C31 and ERDF fund. We would like to thank the BCI laboratory of the University of Essex, especially Dr. John Q. Gan, for allowing us to use their datasets.

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Authors and Affiliations

  1. Department of Computer Architecture and Technology, CITIC, University of Granada (Spain), Granada, Spain

    Alberto Ortega, Juan José Escobar, Julio Ortega, Jesús González & Miguel Damas

  2. Department of Engineering, University of Almería (Spain), Almeria, Spain

    Alfredo Alcayde

  3. Communications Engineering Department, University of Málaga (Spain), Malaga, Spain

    Jorge Munilla

Authors
  1. Alberto Ortega
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  2. Juan José Escobar
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  3. Julio Ortega
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  4. Jesús González
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  5. Alfredo Alcayde
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  6. Jorge Munilla
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  7. Miguel Damas
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Corresponding author

Correspondence to Alberto Ortega .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Ortega, A. et al. (2021). Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88162-7

  • Online ISBN: 978-3-030-88163-4

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