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Brain-Computer Interface Research pp 123–130Cite as

A Brain-Computer-Interface to Combat Musculoskeletal Pain

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

Abstract

Over the past several years, our group has conceived a completely new technological approach toward BCIs aimed at reversing the maladaptive plasticity induced by musculoskeletal pain. The EEG activity patterns of participants with chronic pain (tennis elbow) were differentiated from those of healthy, age and sex matched controls during real-time movement performance. Our results showed a dominance of power in the alpha frequency range only that was significantly correlated with the intensity of pain (visual analogue scale scale—VAS). Based on this novel finding, a neurofeedback system was developed allowing real-time monitoring of alpha power during idle time and movement execution (wrist extensions). Two bars were shown to the patient on a feedback screen—one containing continuous alpha power, the other only alpha power during the preparation phase of movement execution. The goal of the participant was to maintain the alpha power below the initial baseline value during movement execution. Three patients were tested using this system and their pain intensities were monitored. All participants were successful in decreasing their alpha power across days. This was accompanied by a reduction in their perceived pain VAS scores. In summary, we have developed a neurofeedback system for musculoskeletal pain that is capable of providing rapid, accurate and reliable neurofeedback in dynamic conditions, allowing the users to train their brain to reduce the pain.

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Acknowledgements

We wish to acknowledge our participants and all of the students from the laboratory, both past and present.

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Authors and Affiliations

  1. Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, 9220, Aalborg, Denmark

    N. Mrachacz-Kersting, S. Gervasio & K. Dremstrup

  2. Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada

    L. Yao & N. Jiang

  3. Faculty of Medicine, Department of Health Science and Technology, Center for Neuroplasticity and Pain (CNAP), SMI, Aalborg University, Aalborg, Denmark

    T. S. Palsson & T. G. Nielsen

  4. School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    D. Falla

  5. Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK

    D. Farina

Authors
  1. N. Mrachacz-Kersting
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  2. L. Yao
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  3. S. Gervasio
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  4. N. Jiang
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  5. T. S. Palsson
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  6. T. G. Nielsen
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  7. D. Falla
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  8. K. Dremstrup
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  9. D. Farina
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Corresponding author

Correspondence to N. Mrachacz-Kersting .

Editor information

Editors and Affiliations

  1. g.tec Guger Technologies OG , 4521 Schiedlberg, Austria

    Christoph Guger

  2. g.tec Guger Technologies OG, 4521 Schiedlberg, Austria

    Brendan Allison

  3. Biosciences and Informatics, Keio University Biosciences and Informatics, 223-8522 Yokohama, Kazagawa, Japan

    Junichi Ushiba

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Mrachacz-Kersting, N. et al. (2017). A Brain-Computer-Interface to Combat Musculoskeletal Pain. In: Guger, C., Allison, B., Ushiba, J. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-57132-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-57132-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57131-7

  • Online ISBN: 978-3-319-57132-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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