Identifiction of specific cortico-muscular interactions is es- sential for understanding sensorimotor control. These inter- actions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously un- der a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal rep- resentation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.
|Title of host publication
|IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
|Number of pages
|Published - Apr 2018