  Objective: Functional coupling between the motor cortex and muscle activity
is commonly detected and quantified by cortico-muscular coherence (CMC) or
Granger causality (GC) analysis, which are applicable only to linear couplings
and are not sufficiently sensitive: some healthy subjects show no significant
CMC and GC, and yet have good motor skills. The objective of this work is to
develop measures of functional cortico-muscular coupling that have improved
sensitivity and are capable of detecting both linear and non-linear
interactions. Methods: A multiscale wavelet transfer entropy (TE) methodology
is proposed. The methodology relies on a dyadic stationary wavelet transform to
decompose electroencephalogram (EEG) and electromyogram (EMG) signals into
functional bands of neural oscillations. Then, it applies TE analysis based on
a range of embedding delay vectors to detect and quantify intra- and
cross-frequency band cortico-muscular coupling at different time scales.
Results: Our experiments with neurophysiological signals substantiate the
potential of the developed methodologies for detecting and quantifying
information flow between EEG and EMG signals for subjects with and without
significant CMC or GC, including non-linear cross-frequency interactions, and
interactions across different temporal scales. The obtained results are in
agreement with the underlying sensorimotor neurophysiology. Conclusion: These
findings suggest that the concept of multiscale wavelet TE provides a
comprehensive framework for analysing cortex-muscle interactions. Significance:
The proposed methodologies will enable developing novel insights into movement
control and neurophysiological processes more generally.
