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EEG/ECG Signal Fusion Aimed at Biometric Recognition

  • Silvio BarraEmail author
  • Andrea Casanova
  • Matteo Fraschini
  • Michele Nappi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

The recognition of individuals based on behavioral and biological characteristics has made important strides over the past few years. Growing interest has been recently devoted to the study of physiological measures, which include the electrical activity of brain (EEG) and heart (ECG). Even if the use of multimodal approaches overcome several limitations of traditional uni-modal biometric systems, the simultaneous use of EEG and ECG characteristics has been scarcely investigated. In this paper, we present a set of preliminary results derived by the investigation of a biometric system based on the fusion of simple features simultaneously extracted from EEG and ECG signals. The reported results show high performance both from uni-modal approach (higher performance being EER = 11.17 and EER = 3.83 for EEG and ECG respectively) and fusion (EER = 2.94). However, caution should be considered in the interpretation of the reported results mainly beacuse the analysis was performed on a limited set of subjects.

References

  1. 1.
    Bermudez, T., Lowe, D., Arlaud-Lamborelle, A.-M.: EEG/ECG information fusion for epileptic event detection. In: 2009 16th International Conference on Digital Signal Processing (2009)Google Scholar
  2. 2.
    Chaumon, M., Bishop, D.V.M., Busch, N.A.: A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Journal of Neuroscience Methods (2015)Google Scholar
  3. 3.
    Dewan, M.A.A., Hossain, M.J., Hoque, M., Chae, O.: Contaminated ECG artifact detection and elimination from eeg using energy function based transformation. In: International Conference on Information and Communication Technology, ICICT 2007 (2007)Google Scholar
  4. 4.
    Elgendi, M., Eskofier, B., Dokos, S., Abbott, D.: Revisiting qrs detection methodologies for portable, wearable, battery-operated, and wireless ecg systems. PloS One 9(1) (2014)Google Scholar
  5. 5.
    Fraschini, M., Hillebrand, A., Demuru, M., Didaci, L., Marcialis, G.L.: An EEG-based biometric system using eigenvector centrality in resting state brain networks. Signal Processing Letters. IEEE (2015)Google Scholar
  6. 6.
    Huang, R., Heng, F., Hu, B., Peng, H., Zhao, Q., Shi, Q., Han, J.: Artifacts reduction method in EEG signals with wavelet transform and adaptive filter. In: Ślȩzak, D., Tan, A.-H., Peters, J.F., Schwabe, L. (eds.) BIH 2014. LNCS, vol. 8609, pp. 122–131. Springer, Heidelberg (2014) Google Scholar
  7. 7.
    La Rocca, D., Campisi, P., Vegso, B., Cserti, P., Kozmann, G., Babiloni, F.,and De Vico Fallani, F.: Human brain distinctiveness based on eeg spectral coherence connectivity. IEEE Transactions on Biomedical Engineering (2014)Google Scholar
  8. 8.
    Mporas, I., Tsirka, V., Zacharaki, E., Koutroumanidis, M., Megalooikonomou, V.: Evaluation of time and frequency domain features for seizure detection from combined EEG and ECG signals. In: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (2014)Google Scholar
  9. 9.
    Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., Richardson, M., Megalooikonomou, V.: Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients. Expert Systems with Applications (2015)Google Scholar
  10. 10.
    Muthukumaraswamy, S.: High-frequency brain activity and muscle artifacts in meg/eeg: A review and recommendations. Frontiers in Human Neuroscience 7, 138 (2013)CrossRefGoogle Scholar
  11. 11.
    Park, H.-J., Jeong, D.-U., Park, K.-S.: Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE Transactions on Biomedical Engineering (2002)Google Scholar
  12. 12.
    Del Pozo-Banos, M., Alonso, J.B., Ticay-Rivas, J.R., Travieso, C.M.: Electroencephalogram subject identification: A review. Expert Systems with Applications (2014)Google Scholar
  13. 13.
    Raofen, W., Jianhua, Z., Xingyu, W.: Automatic ocular artifact suppression from human operator’s eeg based on a combination of independent component analysis and fuzzy c-means clustering techniques. In: 2011 30th Chinese Control Conference (CCC) (2011)Google Scholar
  14. 14.
    Riera, A., Dunne, S., Cester, I., Ruffini, G.: Starfast: a wire-less wearable EEG/ECG biometric system based on the enobio sensor. In: Proceedings of the International Workshop on Wearable Micro and Nanosystems for Personalised Health (2008)Google Scholar
  15. 15.
    Riera, A., Soria-Frisch, A., Caparrini, M., Cester, I., Ruffini, G.: 1 multimodal physiological biometrics authentication. Biometrics: Theory, Methods, and Applications (2009)Google Scholar
  16. 16.
    Ross, A., Jain, A.K.: Multimodal biometrics: an overview. In: 2004 12th European Signal Processing Conference (2004)Google Scholar
  17. 17.
    Shahid, S., Prasad, G., Sinha, R.K.: On fusion of heart and brain signals for hybrid BCI. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER) (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Silvio Barra
    • 1
    Email author
  • Andrea Casanova
    • 1
  • Matteo Fraschini
    • 2
  • Michele Nappi
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly
  2. 2.Department of Electrical and Electronic Engineering (DIEE)University of CagliariCagliariItaly
  3. 3.University of SalernoFisciano, SalernoItaly

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