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    Can Face Swapping Technology Facilitate Mental Imagery Training?

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    Date
    2017
    Author
    Matsumura, Haruka
    Watanabe, Hironori
    Chen, Tai Chih
    Taketomi, Takafumi
    Yoshitake, Yasuhide
    Plopski, Alexandor
    Sandor, Christian
    Kato, Hirokazu
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    Abstract
    In this research, we conducted a preliminary study to investigate the effectiveness of face swapping technology for mental imagery training. To confirm its effectiveness, we used transcranial magnetic stimulation for measuring motor evoked potential (MEP) as brain excitability during mental imagery training. In the experiment, we used three motions: wrist dorsiflexion as an easy-to-perform motion, and pen spinning and baoding balls rotating motions as difficult motions. In each target motion, we compared MEPs when watching own motion video, another person's motion video, and another person's motion video with the face swapped with own face. The results showed that there was a difference between MEPs in difficult motion video observations. Watching another person's motion video with face swapping showed higher MEP than simply watching another person's video.
    BibTeX
    @inproceedings {10.2312:egve.20171367,
    booktitle = {ICAT-EGVE 2017 - Posters and Demos},
    editor = {Tony Huang and Arindam Dey},
    title = {{Can Face Swapping Technology Facilitate Mental Imagery Training?}},
    author = {Matsumura, Haruka and Watanabe, Hironori and Chen, Tai Chih and Taketomi, Takafumi and Yoshitake, Yasuhide and Plopski, Alexandor and Sandor, Christian and Kato, Hirokazu},
    year = {2017},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-052-9},
    DOI = {10.2312/egve.20171367}
    }
    URI
    http://dx.doi.org/10.2312/egve.20171367
    https://diglib.eg.org:443/handle/10.2312/egve20171367
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA