Abbas, Farwa

Cvetkovic, Zoran

Dai, Wei

McClelland, Verity

English

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

King's Research Portal

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

https://kclpure.kcl.ac.uk/portal/files/200516963/SS_ADMM_STATIONARY_AND_SPARSE_GRANGER_CAUSAL_DISCOVERY_FOR_CORTICOMUSCULAR_COUPLING_FINAL.pdf

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

Abstract

Similar works

Full text

Available Versions

King's Research Portal