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EEG-Based Subjects Identification Based on Biometrics of Imagined Speech Using EMD

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Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

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Abstract

When brain activity ions, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a Biometric Recognition System. For each EEG channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on the Minkowski distance, and for each IMF 4 features are computed: Instantaneous and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To test the proposed method, a dataset with 20 Subjects who imagined 30 repetitions of 5 words in Spanish, is used. Four classifiers are used for this task - random forest, SVM, naive Bayes, and k-NN - and their performances are compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds cross-validation suggest that the proposed method based on EMD can be valuable for creating EEG-based biometrics of imagined speech for Subject identification.

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Acknowledgments

This work was supported by Enabling Technologies - NTNU, under the project “David versus Goliath: single-channel EEG unravels its power through adaptive signal analysis - FlexEEG”.

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Authors and Affiliations

  1. Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway

    Luis Alfredo Moctezuma & Marta Molinas

Authors
  1. Luis Alfredo Moctezuma
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  2. Marta Molinas
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Corresponding author

Correspondence to Luis Alfredo Moctezuma .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

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Moctezuma, L.A., Molinas, M. (2018). EEG-Based Subjects Identification Based on Biometrics of Imagined Speech Using EMD. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_43

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  • DOI: https://doi.org/10.1007/978-3-030-05587-5_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05586-8

  • Online ISBN: 978-3-030-05587-5

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