Cortico-muscular communication patterns reveal important informa- tion about motor control. However, inferring significant causal re- lationships between motor cortex electroencephalogram (EEG) and surface electromyogram (sEMG) of concurrently active muscles is challenging since relevant processes involved in muscle control are relatively weak compared to additive noise and background activi- ties. In this paper, a framework for identification of cortico-muscular linear time invariant communication is proposed that simultaneously estimates model order and its parameters by enforcing sparsity and stationarity conditions in a convex optimization program. The exper- imental results demonstrate that our proposed algorithm outperforms existing techniques for autoregressive model estimation, in terms of computational speed and model identification for causality estima- tion.
|Title of host publication||ICASSP 2023|
|Number of pages||5|
|Publication status||Accepted/In press - 16 Feb 2023|