Article

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.
CI - (c) The Author(s) 2015.

Langue : ANGLAIS