Impact of shoulder abduction loading on brain-machine interface in predicting hand opening and closing in individuals with chronic stroke

Jun Yao*, Clay Sheaff, Carolina Carmona, Julius P A Dewald

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background. Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy - flexor synergy - is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors. Objective. We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy). Methods. We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke. Results. We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task. Conclusions. This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke.

Original languageEnglish (US)
Pages (from-to)363-372
Number of pages10
JournalNeurorehabilitation and Neural Repair
Volume30
Issue number4
DOIs
StatePublished - Jan 1 2016

Fingerprint

Brain-Computer Interfaces
Hand
Stroke
Arm
Elbow
Wrist
Upper Extremity
Fingers

Keywords

  • basic hand function
  • brain-machine interface
  • loss of independent joint control
  • muscle synergies
  • stroke

ASJC Scopus subject areas

  • Rehabilitation
  • Neurology
  • Clinical Neurology

Cite this

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title = "Impact of shoulder abduction loading on brain-machine interface in predicting hand opening and closing in individuals with chronic stroke",
abstract = "Background. Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy - flexor synergy - is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors. Objective. We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy). Methods. We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke. Results. We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task. Conclusions. This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke.",
keywords = "basic hand function, brain-machine interface, loss of independent joint control, muscle synergies, stroke",
author = "Jun Yao and Clay Sheaff and Carolina Carmona and Dewald, {Julius P A}",
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T1 - Impact of shoulder abduction loading on brain-machine interface in predicting hand opening and closing in individuals with chronic stroke

AU - Yao, Jun

AU - Sheaff, Clay

AU - Carmona, Carolina

AU - Dewald, Julius P A

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Background. Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy - flexor synergy - is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors. Objective. We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy). Methods. We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke. Results. We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task. Conclusions. This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke.

AB - Background. Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy - flexor synergy - is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors. Objective. We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy). Methods. We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke. Results. We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task. Conclusions. This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke.

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