SS-ADMM: STATIONARY AND SPARSE GRANGER CAUSAL DISCOVERY FOR CORTICO-MUSCULAR COUPLING

Cortico-muscular communication patterns reveal important information about motor control. However, inferring significant causal relationships between motor cortex electroencephalogram (EEG) signals and surface electromyogram (sEMG) signals of concurrently active muscles is challenging since relevant processes involved in muscle control are relatively weak compared to additive noise and background activities. In this paper, a framework for identification of cortico-muscular linear time invariant communication channel is proposed that simultaneous estimates model order and its parameters by enforcing sparsity and stationarity conditions in a convex optimization program. The experimental results demonstrate that our proposed algorithm outperforms the existing techniques for auto regressive model estimation, in terms of computational speed and model identification for causality estimation.