Skip to main content
SpringerLink
Log in
Book cover

International Conference Automation

ICA 2017: Automation 2017 pp 393–400Cite as

Characteristics of Question of Blind Source Separation Using Moore-Penrose Pseudoinversion for Reconstruction of EEG Signal

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 550))

Abstract

The paper presents question of blind source separation encountered by researchers aiming to determine location of generation electric activity in human brain as a source signal characteristic for given neuron fraction. To that end, Blind Signal Separation (BSS) technique with Moore-Penrose pseudoinversion was presented. The technique is useful for reconstruction of EEG signal. For the experimental purpose, sLORETA algorithm was also used to identify sources as a part of the inverse problem.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Mowla, M.R., Ng, S., Zilany, M., Paramesran, R.: Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomed. Signal Process. Control 22, 111–118 (2015)

    Article  Google Scholar 

  2. Cardoso, J.F.: Source separation using higher order moments. Proc. IEEE ICASSP 4, 2109–2112 (1989)

    Google Scholar 

  3. Ruiz, R.A.S., Ranta, R., Louis-Dorr, V.: EEG montage analysis in the blind source separation framework. Biomed. Signal Process. Control 6(1), 77–84 (2011)

    Article  Google Scholar 

  4. Ameri, R., Pouyan, A., Abolghasemi, V.: Projective dictionary pair learning for EEG signal classification in brain computer interface applications. Neurocomputing 218, 382–389 (2016)

    Article  Google Scholar 

  5. Cichocki, A., Georgiev, P.: Blind source separation algorithms with matrix constraints. IEICE Trans. Fundam. E86–A, 1–9 (2003)

    Google Scholar 

  6. Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, Oxford (2010)

    Google Scholar 

  7. Hwang, W., Ho, J.: Null space component analysis for noisy blind source separation. Signal Process. 109, 301–316 (2015)

    Article  Google Scholar 

  8. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    Article  Google Scholar 

  9. Fitzgibbon, S.P., Powers, D.M., Pope, K.J., Clark, C.R.: Removal of EEG noise and artifact using blind source separation. J. Clin. Neurophysiol. 24, 232–243 (2007)

    Article  Google Scholar 

  10. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)

    Book  Google Scholar 

  11. Jung, T.P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T.J.: Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin. Neurophysiol. 111, 1745–1758 (2000)

    Article  Google Scholar 

  12. Koles, Z.J., Soong, A.: EEG source localization: implementing the spatio-temporal decomposition approach. Electroencephalogr. Clin. Neurophysiol. 107, 343–352 (1998)

    Article  Google Scholar 

  13. Li, Y., Cichocki, A., Amari, S.I.: Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach. IEEE Trans. Neural Netw. 17(2), 419–431 (2006)

    Article  Google Scholar 

  14. da Silva, F.L.: Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. Magn. Res. Img. 22, 1533–1538 (2004)

    Article  Google Scholar 

  15. Molgedey, L., Schuster, H.G.: Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634–3636 (1994)

    Article  Google Scholar 

  16. Mller, K.R., Vigario, R., Meinecke, F., Ziehe, A.: Blind source separation techniques for decomposing event-related brain signals. Int. J. Bifurcat. Chaos 14, 773–791 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Pascual-Marqui, R.D.: Review of methods for solving the EEG inverse problem. Int. J. Bioelectromagn. 1, 7586 (1999)

    Google Scholar 

  18. Liu, H., Xie, X., Xu, S., Wan, F., Hu, Y.: One-unit second-order blind identification with reference for short transient signals. Inform.Sci. 227, 90–101 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pham, D.T., Cardoso, J.F.: Blind separation of instantaneous mixtures of non stationary sources. IEEE Trans. Signal Process. 49(9), 1837–1848 (2001)

    Article  MathSciNet  Google Scholar 

  20. Tan, D.S., Nijholt, A. (eds.): Brain-Computer Interfaces. Springer, London (2012)

    Google Scholar 

  21. Yeredor, A.: Second-order methods based on color. In: Comon, P., Jutten, C. (eds.) Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, Oxford (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Automatic Control and Informatics, Institute of Control and Computer Engineering, Opole University of Technology, Prszkowska 76, 45-271, Opole, Poland

    Szczepan Paszkiel

Authors
  1. Szczepan Paszkiel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Szczepan Paszkiel .

Editor information

Editors and Affiliations

  1. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Roman Szewczyk

  2. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Cezary Zieliński

  3. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Małgorzata Kaliczyńska

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Paszkiel, S. (2017). Characteristics of Question of Blind Source Separation Using Moore-Penrose Pseudoinversion for Reconstruction of EEG Signal. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-54042-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54042-9_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54041-2

  • Online ISBN: 978-3-319-54042-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation