Machine Vision and Applications

, Volume 27, Issue 8, pp 1351–1360 | Cite as

EEG signal preprocessing for biometric recognition

  • Emanuele Maiorana
  • Jordi Solé-Casals
  • Patrizio Campisi
Special Issue Paper


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.


Biometrics Electroencephalography (EEG) Preprocessing Artifact removal Blind source separation (BSS) Independent component analysis (ICA) Sample entropy (SE) 



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


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Emanuele Maiorana
    • 1
  • Jordi Solé-Casals
    • 2
  • Patrizio Campisi
    • 1
  1. 1.Section of Applied Electronics, Department of EngineeringRoma Tre UniversityRomeItaly
  2. 2.Data and Signal Processing Research Group, U Sciences TechUniversity of Vic – Central University of CataloniaVicSpain

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