Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

The central nervous system needs to coordinate multiple muscles during postural control. Here we used multivariate information decomposition to investigate the functional interactions in multichannel EMG during these tasks. A set of information measures were estimated from an instantaneous linear regression model and a time- lagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. When comparing the muscle networks across task conditions, we observed significant effects in the muscles involved in performing those tasks: pointing behavior affected upper body muscles and postural behavior mainly affected lower body muscles. However, information decomposition revealed distinct patterns underlying both effects. Manual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information transfer, storage and information in the abductor longus muscles compared to normal stability. These findings show that directed interactions between muscles are widespread and task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss the task-related effects in terms of gain modulations of spinal reflex pathways.