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ICA-Based EEG Spatio-temporal Dipole Source Localization: A Model Study

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

In this paper, we examine the performance of an Independent Component Analysis (ICA) based dipole localization approach to localize multiple source dipoles under noisy environment. Uncorrelated noise of up to 40% was added to scalp EEG signals. The performance of the ICA-based algorithm is compared with the conventional localization procedure using Simplex method. The present simulation results indicate the robustness of the ICA-based approach in localizing multiple dipoles of independent sources.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Jiangsu Polytechnic University, 213016, China

    Ling Zou

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, China

    Shan-An Zhu

  3. Dept. of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Bin He

Authors
  1. Ling Zou
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  2. Shan-An Zhu
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  3. Bin He
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zou, L., Zhu, SA., He, B. (2006). ICA-Based EEG Spatio-temporal Dipole Source Localization: A Model Study. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_83

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  • DOI: https://doi.org/10.1007/11760191_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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