Dictionary Learning Strategies for Cortico-Muscular Coherence Detection and Estimation

被引:0
|
作者
Du, Shengjia [1 ]
Yu, Qi [2 ]
Dai, Wei [3 ]
McClelland, Verity [4 ]
Cvetkovic, Zoran [1 ]
机构
[1] Kings Coll London, Dept Engn, London WC2R 2LS, England
[2] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410073, Hunan, Peoples R China
[3] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England
[4] Kings Coll London, Dept Basic & Clin Neurosci, IoPPN, London WC2R 2LS, England
关键词
EEG; EMG;
D O I
10.1109/EMBC46164.2021.9630090
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页码:240 / 244
页数:5
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