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A Sequential Monte Carlo Approach for Brain Source Localization

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Advances in Intelligent Signal Processing and Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 410))

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Abstract

In this chapter we propose a solution to the Electroencephalography (EEG) inverse problem combining two techniques, which are the Sequential Monte Carlo (SMC) method for estimating the coordinates of the first two non-correlated dominative brain zones (represented by their respective current dipoles) and spatial filtering which is done by beamforming based on EEG data. Beamforming (BF) gives estimates of the respective source moments. In order to validate this novel approach for brain source localization, EEG data from dipoles with known locations and known moments are generated and artificially corrupted with noise. The noise represents the overall influence of other brain sources but they are not brain disturbances. When the power of the EEG signal due to the main brain sources is higher than the summed effect of all other secondary sources, the estimation of the localization of the leading sources is reliable and repetitive over a number of Monte Carlo runs.

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Authors and Affiliations

  1. Institute of Electrical Engineering and Telematics of Aveiro (IEETA), Department of Electronics Telecommunications and Informatics, University of Aveiro, Portugal, Aveiro

    Petia Georgieva, Filipe Silva, Mariofanna Milanova & Nuno Figueiredo

  2. School of Computing and Communications, Lancaster University, InfoLab21, South Drive, Lancaster, LA1 4WA, United Kingdom

    Lyudmila Mihaylova

  3. Computer Science Department, University of Arkansas at Little Rock, 2801 S. University Av, Little Rock, AR, USA, 72204

    Mariofanna Milanova

  4. School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, South Australia, SA, 5095, Australia

    Lakhmi C. Jain

Authors
  1. Petia Georgieva
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  2. Lyudmila Mihaylova
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  3. Filipe Silva
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  4. Mariofanna Milanova
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  5. Nuno Figueiredo
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  6. Lakhmi C. Jain
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Corresponding author

Correspondence to Petia Georgieva .

Editor information

Editors and Affiliations

  1. , Dep. of Electronics Telecommunications, University of Aveiro, Aveiro, 3800 - 193, Portugal

    Petia Georgieva

  2. , School of Computing and Communications, Lancaster University, InfoLab21, South Drive, Lancaster, LA1 4WA, Montserrat

    Lyudmila Mihaylova

  3. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

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Georgieva, P., Mihaylova, L., Silva, F., Milanova, M., Figueiredo, N., Jain, L.C. (2013). A Sequential Monte Carlo Approach for Brain Source Localization. In: Georgieva, P., Mihaylova, L., Jain, L. (eds) Advances in Intelligent Signal Processing and Data Mining. Studies in Computational Intelligence, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28696-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-28696-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28695-7

  • Online ISBN: 978-3-642-28696-4

  • eBook Packages: EngineeringEngineering (R0)

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