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Energy-Time Profiling for Machine Learning Methods to 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

The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of features that could be used to create an intelligent recognition system. Nevertheless, the high number of available features difficult the correct classification of the signals as most of them do not contain relevant information. The use of Feature Selection (FS) techniques allows the reduction of the number of features by extracting a reduced but powerful feature subset. Afterwards, the problem can be addressed by using both the reduced features along with classification methods. In this sense, this work proposes a comparison of five supervised classifiers to solve an Electroencephalography problem in the context of Motor Imagery-based BCI tasks. The predictive models are evaluated in terms of classification rate and performance (execution time and energy consumption), with the idea of determining which alternative offers the best trade-off among all the objectives.

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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, Granada, Spain

    Juan Carlos Gómez-López, Juan José Escobar, Jesús González, Julio Ortega & Miguel Damas

  2. Department of Engineering, University of Almería, Almería, Spain

    Francisco Gil-Montoya

  3. Department of Computer Science, Florida State University, Tallahassee, USA

    Mike Burmester

Authors
  1. Juan Carlos Gómez-López
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  2. Juan José Escobar
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  3. Jesús González
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  4. Francisco Gil-Montoya
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  5. Julio Ortega
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  7. Miguel Damas
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Corresponding author

Correspondence to Juan Carlos Gómez-López .

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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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Gómez-López, J.C. et al. (2021). Energy-Time Profiling for Machine Learning Methods to 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_27

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_27

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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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