Subband Independent Component Analysis for Coherence Enhancement
Subband Independent Component Analysis for Coherence Enhancement
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DOI：
10.1109/tbme.2024.3370638
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发表时间：
2024-08-01
影响因子：
4.6
通讯作者：
Cvetkovic，Zoran
中科院分区：
文献类型：
--
作者：
Guo，Zhenghao;Xu，Yuhang;Cvetkovic，Zoran
皮质-肌肉相干性（Cortico-muscular coherence，CMC）是检测和表征运动皮层和肌肉活动之间功能耦合的常用技术。它通常在受控运动任务期间同步收集的表面肌电图（sEMG）和脑电图（EEG）信号之间进行评估。然而，存在的噪声和活动无关的观察到的运动任务，表面肌电和脑电图结果在低CMC水平，这往往使功能耦合难以检测到。MethodsIn本文中，我们引入相干子带独立成分分析（CoSICA），以提高同步皮质肌肉成分的混合物中捕获的表面肌电和脑电图。该方法依赖于滤波器组处理来将sEMG和EEG信号分解成频带。然后，它适用于独立成分分析沿着与组件选择算法重新合成的表面肌电信号和EEG的设计，以最大限度地提高CMC levels.ResultsWe证明了所提出的方法的有效性，在不同的信号噪声比增加CMC水平首先使用模拟数据。使用神经生理学数据，然后，我们说明，CoSICA处理实现了显着增强原始CMC.ConclusionOur研究结果表明，所提出的技术提供了一个有效的框架，提高相干detection.SignificanceThe建议的方法，最终将有助于理解运动控制，并具有很高的潜力转化为临床实践。
ObjectiveCortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyogram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect.MethodsIn this paper, we introduce Coherent Subband Independent Component Analysis (CoSICA) to enhance synchronous cortico-muscular components in mixtures captured by sEMG and EEG. The methodology relies on filter bank processing to decompose sEMG and EEG signals into frequency bands. Then, it applies independent component analysis along with a component selection algorithm for re-synthesis of sEMG and EEG designed to maximize CMC levels.ResultsWe demonstrate the effectiveness of the proposed method in increasing CMC levels across different signal-to-noise ratios first using simulated data. Using neurophysiological data, we then illustrate that CoSICA processing achieves a pronounced enhancement of original CMC.ConclusionOur findings suggest that the proposed technique provides an effective framework for improving coherence detection.SignificanceThe proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice.