Abstract
In this work a time-frequency approach to estimate the Cortico-Muscular Coherence for the detection of the movement intent is presented, assessed on simulated data, and evaluated experimentally during different motor tasks performed by healthy subjects and patients suffering from different types of tremor. Cortico-Muscular Coherence is an index of the coupling of EEG signal in the cortical area with sEMG activity in the frequency domain, and its contributions in the beta band (15–30 Hz) have been associated to the movement intent. Cortico-Muscular Coherence estimation is here achieved by considering a closed-loop representation of the signals under analysis obtained through Multivariate Auto Regressive modeling. Significance levels for Cortico-Muscular Coherence are assessed by means of a surrogate data analysis approach. The detection technique is able to reveal significant Cortico-Muscular Coherence changes in 79% of the experimental trials, with a mean anticipation of 1.35 s with respect to movement onset. Time-frequency estimation of Cortico-Muscular Coherence can provide an insight for the development of a multimodal BCI able to integrate information from the brain activity in the functioning of assistive devices.
Highlights
In the last years, many efforts have been dedicated to the study of how the motor cortex controls and synchronizes muscle activity during voluntary movements in humans
In this work a method based on bivariate autoregressive (BAR) modelling for the estimation of time-frequency Cortico-Muscular Coherence (CMC) has been applied and tested on simulated signals and on data recorded on two healthy subjects and four patients affected by tremor impairment
Analysis on data from experimental trials shows how coherence contributions in the beta-band, which have been linked in literature to the execution of voluntary movements, show significant values before and during the actual execution of the tasks
Summary
Many efforts have been dedicated to the study of how the motor cortex controls and synchronizes muscle activity during voluntary movements in humans. Magnetoencephalographic recordings (MEG) in man show CMC between the controlateral cortical signal from the primary motor cortex and EMG activity in the beta band (15–30 Hz) during weak contractions of forearm and hand muscles [2, 3]. These results have been obtained using EEG signals [4]. In order to overcome the limitations of the standard Welch’s approach, in this work we would like to apply a method based on bivariate auto-regressive (BAR) modeling [12] for the estimation of time-frequency CMC. CMC is proposed as a solution for the detection of movement intent in tremor-affected patients and healthy subjects
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