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EEG signal preprocessing for biometric recognition

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Abstract

Electroencephalography (EEG) has been recently investigated as a biometric modality for automatic people recognition purposes. Several studies have shown that brain signals possess subject-specific traits that allow distinguishing people. Nonetheless, extracting discriminative characteristics from EEG recordings may be a challenging task, due to the significant presence of artifacts in the acquired data. In order to cope with such issue, in this paper we evaluate the effectiveness of some preprocessing techniques in automatically removing undesired EEG contributions, with the aim of improving the achievable recognition rates. Specifically, methods based on blind source separation and sample entropy estimation are here investigated. An extensive set of experimental tests, performed over a large database comprising recordings taken from 50 healthy subjects during three distinct sessions spanning a period of about one month, in both eyes-closed and eyes-open conditions, is carried out to analyze the performance of the proposed approaches.

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Acknowledgments

The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X used for this research.

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Authors and Affiliations

  1. Section of Applied Electronics, Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146, Rome, Italy

    Emanuele Maiorana & Patrizio Campisi

  2. Data and Signal Processing Research Group, U Sciences Tech, University of Vic – Central University of Catalonia, Perot Rocaguinarda 17, 08500, Vic, Catalonia, Spain

    Jordi Solé-Casals

Authors
  1. Emanuele Maiorana
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  2. Jordi Solé-Casals
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  3. Patrizio Campisi
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Correspondence to Emanuele Maiorana.

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Maiorana, E., Solé-Casals, J. & Campisi, P. EEG signal preprocessing for biometric recognition. Machine Vision and Applications 27, 1351–1360 (2016). https://doi.org/10.1007/s00138-016-0804-4

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  • DOI: https://doi.org/10.1007/s00138-016-0804-4

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