Annals of Biomedical Engineering

, Volume 42, Issue 10, pp 2095–2105 | Cite as

Performance Assessment of a Brain–Computer Interface Driven Hand Orthosis

  • Christine E. King
  • Kunal R. Dave
  • Po T. Wang
  • Masato Mizuta
  • David J. Reinkensmeyer
  • An H. Do
  • Shunji Moromugi
  • Zoran Nenadic
Article
First Online:
Received:
Accepted:

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.

Keywords

Electroencephalography (EEG) Stroke Distal upper extremity weakness Neuro-rehabilitation 

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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Notes

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.

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Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Christine E. King
    • 1
  • Kunal R. Dave
    • 1
  • Po T. Wang
    • 1
  • Masato Mizuta
    • 2
  • David J. Reinkensmeyer
    • 1
    • 3
    • 4
  • An H. Do
    • 5
  • Shunji Moromugi
    • 6
  • Zoran Nenadic
    • 1
    • 7
  1. 1.Department of Biomedical EngineeringUniversity of California, Irvine (UCI)IrvineUSA
  2. 2.Department of Civil Engineering and Engineering MechanicsColumbia UniversityNew YorkUSA
  3. 3.Department of Mechanical and Aerospace EngineeringUCIIrvineUSA
  4. 4.Department of Anatomy and NeurobiologyUCIIrvineUSA
  5. 5.Department of NeurologyUCIIrvineUSA
  6. 6.Department of Mechanical Systems EngineeringNagasaki UniversityNagasakiJapan
  7. 7.Department of Electrical Engineering and Computer ScienceUCIIrvineUSA

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