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Performance Assessment of a Brain–Computer Interface Driven Hand Orthosis

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Abstract

Stroke survivors are typically affected by hand motor impairment. Despite intensive rehabilitation and spontaneous recovery, improvements typically plateau a year after a stroke. Therefore, novel approaches capable of restoring or augmenting lost motor behaviors are needed. Brain–computer interfaces (BCIs) may offer one such approach by using neurophysiological activity underlying hand movements to control an upper extremity orthosis. To test the performance of such a system, we developed an electroencephalogram-based BCI controlled electrically actuated hand orthosis. Six able-bodied participants voluntarily grasped/relaxed one hand to elicit BCI-mediated closing/opening of the orthosis mounted on the opposite hand. Following a short training/calibration procedure, participants demonstrated real-time, online control of the orthosis by following computer cues. Their performances resulted in an average of 1.15 (standard deviation: 0.85) false alarms and 0.22 (0.36) omissions per minute. Analysis of signals from electrogoniometers mounted on both hands revealed an average correlation between voluntary and BCI-mediated movements of 0.58 (0.13), with all but one online performance being statistically significant. This suggests that a BCI driven hand orthosis is feasible, and therefore should be tested in stroke individuals with hand weakness. If proven viable, this technology may provide a novel approach to the neuro-rehabilitation of hand function after stroke.

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Abbreviations

BCI:

Brain–computer interface

FES:

Functional electrical stimulation

PSD:

Power spectral density

CPCA:

Classwise principal component analysis

AIDA:

Approximate information discriminant analysis

LDA:

Linear discriminant analysis

AD :

Analysis duration

PD :

Posterior probability averaging duration

SD:

Standard deviation

FA:

False alarm

OM:

Omission

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Acknowledgments

KRD received financial support from the UCI Summer Undergraduate Research Program. DJR reports personal fees and other from Hocoma A.G., Grants, personal fees and other from Flint Rehabilitation Devices; outside the submitted work, DJR has a patent application for an arm exoskeleton for rehabilitation after stroke with royalties paid by Hocoma.

Conflicts of interest

All other authors declare no financial conflicts of interest.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, University of California, Irvine (UCI), Irvine, CA, USA

    Christine E. King, Kunal R. Dave, Po T. Wang, David J. Reinkensmeyer & Zoran Nenadic

  2. Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY, USA

    Masato Mizuta

  3. Department of Mechanical and Aerospace Engineering, UCI, Irvine, CA, USA

    David J. Reinkensmeyer

  4. Department of Anatomy and Neurobiology, UCI, Irvine, CA, USA

    David J. Reinkensmeyer

  5. Department of Neurology, UCI, Irvine, CA, USA

    An H. Do

  6. Department of Mechanical Systems Engineering, Nagasaki University, Nagasaki, Japan

    Shunji Moromugi

  7. Department of Electrical Engineering and Computer Science, UCI, Irvine, CA, USA

    Zoran Nenadic

Authors
  1. Christine E. King
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  2. Kunal R. Dave
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  3. Po T. Wang
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  4. Masato Mizuta
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  5. David J. Reinkensmeyer
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  6. An H. Do
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  7. Shunji Moromugi
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  8. Zoran Nenadic
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Corresponding author

Correspondence to Zoran Nenadic.

Additional information

Associate Editor Zahra Moussavi oversaw the review of this article.

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King, C.E., Dave, K.R., Wang, P.T. et al. Performance Assessment of a Brain–Computer Interface Driven Hand Orthosis. Ann Biomed Eng 42, 2095–2105 (2014). https://doi.org/10.1007/s10439-014-1066-9

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  • DOI: https://doi.org/10.1007/s10439-014-1066-9

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