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

Farwa Abbas; Verity McClelland; Zoran Cvetkovic; Wei Dai

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 proceeding › Conference paper › peer-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 language	English
Title of host publication	ICASSP 2023
Publisher	IEEE
Number of pages	5
Publication status	Accepted/In press - 16 Feb 2023

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SS-ADMM STATIONARY AND SPARSE GRANGER CAUSAL DISCOVERY FOR CORTICOMUSCULAR COUPLING - FINALAccepted author manuscript, 1.07 MB

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title = "SS-ADMM: STATIONARY AND SPARSE GRANGER CAUSAL DISCOVERY FOR CORTICO-MUSCULAR COUPLING",

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.",

author = "Farwa Abbas and Verity McClelland and Zoran Cvetkovic and Wei Dai",

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AU - Abbas, Farwa

AU - McClelland, Verity

AU - Cvetkovic, Zoran

AU - Dai, Wei

PY - 2023/2/16

Y1 - 2023/2/16

N2 - 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.

AB - 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.

M3 - Conference paper

BT - ICASSP 2023

PB - IEEE

ER -

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

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