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Supervised EEG Source Imaging with Graph Regularization in Transformed Domain

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Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

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Abstract

It is of great significance to infer activation extents under different cognitive tasks in neuroscience research as well as clinical applications. However, the EEG electrodes measure electrical potentials on the scalp instead of directly measuring activities of brain sources. To infer the activated cortex sources given the EEG data, many approaches were proposed with different neurophysiological assumptions. Traditionally, the EEG inverse problem was solved in an unsupervised way without any utilization of the brain status label information. We propose that by leveraging label information, the task related discriminative extended source patches can be much better retrieved from strong spontaneous background signals. In particular, to find task related source extents, a novel supervised EEG source imaging model called Graph regularized Variation-Based Sparse Cortical Current Density (GVB-SCCD) was proposed to explicitly extract the discriminative source extents by embedding the label information into the graph regularization term. The graph regularization was derived from the constraint that requires consistency for all the solutions on different time points within the same class. An optimization algorithm based on the alternating direction method of multipliers (ADMM) is derived to solve the GVB-SCCD model. Numerical results show the effectiveness of our proposed framework.

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Author information

Authors and Affiliations

  1. Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, TX, USA

    Feng Liu, Shouyi Wang & Jay Rosenberger

  2. Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA

    Jing Qin

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

Authors
  1. Feng Liu
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  2. Jing Qin
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  3. Shouyi Wang
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  4. Jay Rosenberger
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  5. Jianzhong Su
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Corresponding author

Correspondence to Shouyi Wang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Liu, F., Qin, J., Wang, S., Rosenberger, J., Su, J. (2017). Supervised EEG Source Imaging with Graph Regularization in Transformed Domain. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

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