Bayesian Network Modeling For Discovering "Directed Synergies" Among Muscles in Reaching Movements

Modeling the muscle activity patterns in coordinated reaching movements from surface electromyogram (sEMG) recordings is a key challenge in motor behavior studies. Based on Bayesian network (BN) modeling of sEMG data, this paper presents a framework for discovering and modeling muscle networks and identifying functional muscle groupings. The learned network is further explored for the purpose of classification. We demonstrate the proposed approach on reaching movements in stroke. We found that the specific muscle triples (anterior deltoid, biceps-brachium and lateral deltoid), (biceps-brachium, triceps lateral and lateral deltoid) and (triceps long head, triceps lateral and lateral deltoid, are selectively recruited during reaching movements and are differentially recruited after stroke. We call these computed muscle triplets "directed synergies" to contrast with synergies that are defined by traditional covariance methods. A BN trained on a single healthy subject completely classified and detected the affected side in all stroke subjects. The proposed approach appears a promising technique for muscle network and synergy analysis in motor control