Dictionary Learning Strategies for Cortico-Muscular Coherence Detection and Estimation
Dictionary Learning Strategies for Cortico-Muscular Coherence Detection and Estimation
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DOI：
10.1109/embc46164.2021.9630090
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Shengjia Du;Qi Yu;Wei Dai;V. McClelland;Z. Cvetkovic
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文献类型：
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作者：
Shengjia Du;Qi Yu;Wei Dai;V. McClelland;Z. Cvetkovic
皮质-肌肉相干性（cortico-muscular coherence，CMC）的频谱方法可以揭示大脑皮层和肌肉外周之间的通信模式，从而为开发运动障碍的新疗法和深入了解基础运动神经科学提供指导。该方法被应用于运动任务期间同步记录的脑电图（EEG）和表面肌电图（sEMG）。然而，与加性噪声和背景活动相比，同步EEG和sEMG分量通常太弱，使得很难检测到显著的相干性。字典学习和稀疏表示已被证明在提高CMC水平方面是有效的。在本文中，我们探讨了最近提出的字典学习算法结合改进的组件选择算法CMC增强的潜力。使用神经生理学数据证明了该方法的有效性，其中CMC水平得到了相当大的改善。
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.