Guo Zhenghao, Xu Yuhang, Rosenzweig Jan, McClelland Verity M, Rosenzweig Ivana, Cvetkovic Zoran
IEEE Trans Biomed Eng. 2024 Aug;71(8):2402-2413. doi: 10.1109/TBME.2024.3370638. Epub 2024 Jul 18.
Cortico-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.
In 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.
We 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.
Our findings suggest that the proposed technique provides an effective framework for improving coherence detection.
The proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice.
皮质-肌肉相干性（CMC）正成为检测和表征运动皮层与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采集的表面肌电图（sEMG）和脑电图（EEG）信号之间进行评估。然而，sEMG和EEG中存在与观察到的运动任务无关的噪声和活动，导致CMC水平较低，这常常使得功能耦合难以检测。
在本文中，我们引入相干子带独立成分分析（CoSICA）来增强sEMG和EEG捕获的混合信号中的同步皮质-肌肉成分。该方法依赖于滤波器组处理，将sEMG和EEG信号分解为不同频段。然后，它应用独立成分分析以及一种成分选择算法，对sEMG和EEG进行重新合成，以最大化CMC水平。
我们首先使用模拟数据证明了所提出方法在不同信噪比下提高CMC水平的有效性。然后，利用神经生理学数据，我们说明了CoSICA处理实现了对原始CMC的显著增强。
我们的研究结果表明，所提出的技术为改善相干性检测提供了一个有效的框架。
所提出的方法最终将有助于理解运动控制，并具有很高的转化为临床实践的潜力。
