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

Farwa Abbas, Verity McClelland, Zoran Cvetkovic, Wei Dai

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationICASSP 2023
PublisherIEEE
Number of pages5
Publication statusAccepted/In press - 16 Feb 2023

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