Reducing the Impact of Shoulder Abduction Loading on the Classification of Hand Opening and Grasping in Individuals with Poststroke Flexion Synergy - Lan, Yiyun; Yao, Jun; Dewald, Julius P. A. (Frontiers in Bioengineering and Biotechnology, 2017)
Application of neural machine interface in individuals with chronic hemiparetic stroke is regarded as a great challenge, especially for classification of the hand opening and grasping during a functional upper extremity movement such as reach-to-grasp. The overall accuracy of classifying hand movements, while actively lifting the paretic arm, is subject to a significant reduction compared to the accuracy when the arm is fully supported. Such a reduction is believed to be due to the expression of flexion synergy, which couples shoulder abduction with elbow/wrist and finger flexion, and is common in up to 60% of the stroke population. Little research has been done to develop methods to reduce the impact of flexion synergy on the classification of hand opening and grasping. In this study, we proposed a novel approach to classify hand opening and grasping in the context of the flexion synergy using a wavelet-coherence-based filter. We first identified the frequency ranges where the coherence between the shoulder abduction muscle and wrist/finger flexion muscles is significant in each participant, and then removed the synergy-induced EMG component with a subject-specific and muscle-specific coherence-based filter. The new approach was tested in 21 stroke individuals with moderate to severe motor impairments. Employing the filter, fourteen participants gained improvement in classification accuracy with a range of 0.1% to 14%, while four showed 0.3% to 1.2% reduction. The remaining three participants were excluded from comparison due to the lack of significant coherence, thus no filters were applied. The improvement in classification accuracy is significant (p=0.017) when the shoulder abduction loading equals 50% of the maximal torque. Our findings suggest that the coherence-based filters can reduce the impact of flexion synergy by removing the synergy-induced EMG component and have the potential to improve the overall classification accuracy of hand movements in individuals with post-stroke flexion synergy.