Functional coupling between the cortex and muscle is com- monly quantified by cortico-muscular coherence (CMC) be- tween 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 sig- nificant 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 al- gorithms 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 pre- served. The effectiveness of the proposed method is as- sessed using simulated and neurophysiological data, where it achieved considerable improvements in coherence levels.
|Title of host publication||ICASSP 2023|
|Number of pages||5|
|Publication status||Accepted/In press - 16 Feb 2023|