Abstract
The spectral method of cortico-muscular coherence (CMC) can reveal the communication patterns between the cerebral cortex and muscle periphery, thus providing guidelines for the development of new therapies for movement disorders and insights into fundamental motor neuroscience. The method is applied to electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously during a motor task. However, synchronous EEG and sEMG components are typically too weak compared to additive noise and background activities making significant coherence very difficult to detect. Dictionary learning and sparse representation have been proved effective in enhancing CMC levels. In this paper, we explore the potential of a recently proposed dictionary learning algorithm in combination with an improved component selection algorithm for CMC enhancement. The effectiveness of the method was demonstrated using neurophysiological data where it achieved considerable improvements in CMC levels.
Original language | English |
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Title of host publication | 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
Publisher | IEEE |
Pages | 240-244 |
Number of pages | 5 |
Volume | 2021 |
DOIs | |
Publication status | Published - 1 Nov 2021 |