Structured Errors-in-Variables Modelling for Cortico-Muscular Coherence Enhancement

TY - CONF

T1 - Structured Errors-in-Variables Modelling for Cortico-Muscular Coherence Enhancement

AU - Guo, Zhenghao

AU - McClelland, Verity M.

AU - Dai, Wei

AU - Cvetkovic, Zoran

N1 - Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - Functional coupling between the cortex and muscle is commonly quantified by cortico-muscular coherence (CMC) between electroencephalogram (EEG) and electromyogram (EMG) signals. However, the presence of noise in EEG and EMG often degrades CMC, making it challenging to detect: some healthy subjects with good motor skills show no significant CMC. This study proposes an approach based on structured errors-in-variables (EIV) modelling to estimate components of the cortex and muscle signals involved in movement control from noisy EEG and EMG signals for the purpose of coherence estimation. We describe three algorithms to identify the underlying EIV system: one based on total least squares; the other two on structured total least squares, in which the Toeplitz data matrix structure is preserved. The effectiveness of the proposed method is assessed using simulated and neurophysiological data, where it achieved considerable improvements in coherence levels.

AB - Functional coupling between the cortex and muscle is commonly quantified by cortico-muscular coherence (CMC) between electroencephalogram (EEG) and electromyogram (EMG) signals. However, the presence of noise in EEG and EMG often degrades CMC, making it challenging to detect: some healthy subjects with good motor skills show no significant CMC. This study proposes an approach based on structured errors-in-variables (EIV) modelling to estimate components of the cortex and muscle signals involved in movement control from noisy EEG and EMG signals for the purpose of coherence estimation. We describe three algorithms to identify the underlying EIV system: one based on total least squares; the other two on structured total least squares, in which the Toeplitz data matrix structure is preserved. The effectiveness of the proposed method is assessed using simulated and neurophysiological data, where it achieved considerable improvements in coherence levels.

KW - Cortico-muscular coherence

KW - EEG

KW - EMG

KW - errors-in-variables models

KW - structured total least squares

UR - http://www.scopus.com/inward/record.url?scp=85177604102&partnerID=8YFLogxK

U2 - 10.1109/ICASSP49357.2023.10095004

DO - 10.1109/ICASSP49357.2023.10095004

M3 - Paper

AN - SCOPUS:85177604102

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

Y2 - 4 June 2023 through 10 June 2023

ER -