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Using a BCI Prosthetic Hand to Control Phantom Limb Pain

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Brain-Computer Interface Research

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

Abstract

Phantom limb pain is neuropathic pain that occurs after the amputation of a limb and partial or complete deafferentation. The underlying cause has been attributed to maladaptive plasticity of the sensorimotor cortex, and evidence suggests that experimental induction of further reorganization should affect the pain. Here, we use a brain–computer interface (BCI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BCI training successfully induced some plastic alteration in the sensorimotor representation of the phantom hand movements. If a patient tried to control the robotic hand by associating the representation of phantom hand movement, it increased the pain while improving classification accuracy of the phantom hand movements. However, if the patient tried to control the robotic hand by associating the representation of the intact hand, it decreased the pain while decreasing the classification accuracy of the phantom hand movements. These results demonstrate that the BCI training controls the phantom limb pain depending on the induced sensorimotor plasticity. Moreover, these results strongly suggest that a reorganization of the sensorimotor cortex is the underlying cause of phantom limb pain.

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Acknowledgements

This research was conducted under the “Development of BMI Technologies for Clinical Application” of SRPBS by MEXT and AMED. This research was also supported in part by JST PRESTO; Grants-in-Aid for Scientific Research KAKENHI (90533802, 24700419, 26560467, 26242088, 22700435, 17H06032, 15H05710, 15H05920); Brain/MINDS and SICP from AMED; ImPACT; Ministry of Health, Labor, and Welfare (18261201); the Japan Foundation of Aging and Health and TERUMO foundation for life sciences and arts.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan

    Takufumi Yanagisawa, Ryohei Fukuma, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Masayuki Hirata, Toshiki Yoshimine & Youichi Saitoh

  2. Division of Functional Diagnostic Science, Osaka University Graduate School of Medicine, Osaka, Japan

    Takufumi Yanagisawa

  3. ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, Kyoto, Japan

    Takufumi Yanagisawa, Ryohei Fukuma & Yukiyasu Kamitani

  4. CiNet Computational Neuroscience Laboratories, Department of Neuroinformatics, Osaka, Japan

    Takufumi Yanagisawa, Ryohei Fukuma, Masayuki Hirata & Toshiki Yoshimine

  5. JST PRESTO, Osaka, Japan

    Takufumi Yanagisawa

  6. Division of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Osaka, Japan

    Takufumi Yanagisawa

  7. Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan

    Ryohei Fukuma & Yukiyasu Kamitani

  8. Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK

    Ben Seymour

  9. Center for Information and Neural Networks, National Institute for Information and Communications Technology, Osaka, Japan

    Ben Seymour

  10. Department of Neuromodulation and Neurosurgery, Graduate School of Medicine, Osaka University, Osaka, Japan

    Koichi Hosomi, Takeshi Shimizu & Youichi Saitoh

  11. Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Tokyo, Japan

    Hiroshi Yokoi

  12. Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Yukiyasu Kamitani

Authors
  1. Takufumi Yanagisawa
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  2. Ryohei Fukuma
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  3. Ben Seymour
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  4. Koichi Hosomi
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  5. Haruhiko Kishima
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  6. Takeshi Shimizu
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  7. Hiroshi Yokoi
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  8. Masayuki Hirata
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  9. Toshiki Yoshimine
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  10. Yukiyasu Kamitani
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  11. Youichi Saitoh
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Corresponding author

Correspondence to Takufumi Yanagisawa .

Editor information

Editors and Affiliations

  1. g.tec Medical Engineering GmbH, Schiedlberg, Austria

    Christoph Guger

  2. Department of Health Science and Technology, Aalborg University, Aalborg, Denmark

    Natalie Mrachacz-Kersting

  3. Department of Cognitive Science, University of California, San Diego, CA, USA

    Brendan Z. Allison

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© 2019 The Author(s), under exclusive licence to Springer Nature Switzerland AG

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Yanagisawa, T. et al. (2019). Using a BCI Prosthetic Hand to Control Phantom Limb Pain. In: Guger, C., Mrachacz-Kersting, N., Allison, B. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-05668-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-05668-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05667-4

  • Online ISBN: 978-3-030-05668-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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