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Current Design with Minimum Error in Transcranial Direct Current Stimulation

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Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

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Abstract

As a non-invasive brain stimulation technology, transcanial direct current stimulation (tDCS) has been recently attracting more and more attention in research and clinic applications due to its convenient implementation and modulation of the brain functionality. In this paper, we propose a novel multi-electrode tDCS current configuration model that minimizes the total error under the safety constraints. After rewriting the model as a linearly constrained minimization problem, we develop an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM). Numerical experiments have shown the great potential of the proposed method in terms of accuracy and focality.

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Acknowledgments

The research of Jing Qin is supported by the NSF grant DMS-1818374.

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Authors and Affiliations

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

    Jing Qin

  2. Department of Bioengineering, University of California, Los Angeles, CA, USA

    Yushan Wang & Wentai Liu

Authors
  1. Jing Qin
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  2. Yushan Wang
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  3. Wentai Liu
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Corresponding author

Correspondence to Jing Qin .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

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

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

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Qin, J., Wang, Y., Liu, W. (2018). Current Design with Minimum Error in Transcranial Direct Current Stimulation. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-05587-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05586-8

  • Online ISBN: 978-3-030-05587-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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