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

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.