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Brain-Computer Interface Research pp 53–61Cite as

Restoration of Finger and Arm Movements Using Hybrid Brain/Neural Assistive Technology in Everyday Life Environments

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

Abstract

Controlling advanced robotic systems with brain signals promises substantial improvements in health care, for example, to restore intuitive control of hand movements after severe stroke or spinal cord injuries (SCI). However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. The main challenge to integrate such systems in everyday life environments relates to the reliability of brain-control, particularly when brain signals are recorded non-invasively. Using a non-invasive, hybrid EEG-EOG-based brain/neural hand exoskeleton (B/NHE), we demonstrate full restoration of activities of daily living (ADL), such as eating and drinking, across six paraplegic individuals (five males, 30 ± 14 years) outside the laboratory. In a second set of experiments, we show that even whole-arm exoskeleton control is feasible and safe by combining hybrid brain/neural control with vision-guided and context-sensitive autonomous robotics. Given that recent studies indicate neurological recovery after chronic stroke or SCI when brain-controlled assistive technology is repeatedly used for 1–12 months, we suggest that combining an assistive and rehabilitative approach may further promote brain-machine interface (BMI) technology as a standard therapy option after stroke and SCI. In such scenario, brain/neural-assistive technology would not only have an immediate impact on the quality of life and autonomy of individuals with brain or spinal cord lesions but would also foster neurological recovery by stimulating functional and structural neuroplasticity.

Keywords

  • Brain/neural hand exoskeleton (B/NHE)
  • Spinal cord injury (SCI)
  • Stroke
  • Activities of daily living (ADL)
  • Neural recovery

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Fig. 1

(From Ref. [21], Science Robotics)

Fig. 2

Notes

  1. 1.

    https://www.youtube.com/watch?v=zs5k7MpS1g0.

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Acknowledgements

This chapter and the presented studies were supported by the European Commission under the project AIDE (G.A. no: 645322), the European Research Council (ERC) under the project NGBMI (759370), and the Baden-Württemberg Stiftung (NEU007/1). SRS received special support by the Brain & Behavior Research Foundation as 2017 NARSAD Young Investigator Grant recipient and P&S Fund Investigator.

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Authors and Affiliations

  1. Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany

    Surjo R. Soekadar & Marius Nann

  2. Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Tübingen, Germany

    Surjo R. Soekadar & Marius Nann

  3. The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Simona Crea, Emilio Trigili & Nicola Vitiello

  4. Hospital de Neurorehabilitació Institut Guttmann, Barcelona, Spain

    Cristina Gómez & Eloy Opisso

  5. Human Cortical Physiology and Stroke Neurorehabilitation Section, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA

    Leonardo G. Cohen

  6. Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany

    Niels Birbaumer

  7. Wyss Center for Bio and Neuroengineering, Geneva, Switzerland

    Niels Birbaumer

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  1. Surjo R. Soekadar
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  2. Marius Nann
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Correspondence to Surjo R. Soekadar .

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Editors and Affiliations

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

    Dr. Christoph Guger

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

    Dr. Natalie Mrachacz-Kersting

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

    Dr. Brendan Z. Allison

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Soekadar, S.R. et al. (2019). Restoration of Finger and Arm Movements Using Hybrid Brain/Neural Assistive Technology in Everyday Life Environments. 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_5

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  • DOI: https://doi.org/10.1007/978-3-030-05668-1_5

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