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

  • Luis Alfredo Moctezuma
  • Marta Molinas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

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.

Keywords

Biometric security Subject identification Imagined speech Electroencephalograms (EEG) Empirical Mode Decomposition (EMD) 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

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