Relevance Network Modeling for Muscle Association Pattern in Reaching Movements

Our purpose is to study how different muscles collaborate together to efficiently create a smooth, coordinated reaching movement. In the EMG literature, it has been commonplace to model the relationships between muscles using correlation and frequency-based measures such as coherence. Inspired by the observation that mutual information is a more general and reliable metric in revealing complex relationships between time series, we propose a relevance network framework for modeling temporally-aligned multi-variate sEMG recordings. Such a network can identify functional muscle associations, providing insights into the underlying motor behavior. Here we demonstrate that relevance networks can: 1) detect the effects of handedness in normal subjects, and 2) robustly detect between the healthy and stroke subjects. Specifically, the structural features of muscle associations were sensitive to handedness and disease status yet relatively robust to differences across subjects - a long-standing goal in rehabilitation research. These results warrant further study to more fully determine the extent to which the relevance networks may elucidate the complex muscle interactions in reaching movements.